R Notebook: Garter Snake, Heat Stress, MetaAnalysis of Differential Gene Expression of RNAseq (2008 and 2012) mapped to the T.elegans Genome

Date: 2022-05-22 Author: Tonia Schwartz

Edited from: Law, C. W., Alhamdoosh, M., Su, S., Dong, X., Tian, L., Smyth, G. K., et al. (2018). RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Res 5, 1408. doi:10.12688/f1000research.9005.3.

Set-up

Clear the workspace and set the directory

rm()
setwd("~/Box/SchwartzLab/Projects/TS_Lab_GarterSnakes/2020_GarterSnake_GeneExpression_SNPs_IIS/Analyses/GeneExpression_Analyses/To_Genome/3_DifferentialGeneExpresssion/2022-05-22_PrepPub")

Install Packages

#```{r} #Install packages if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”) BiocManager::install()

if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”) #BiocManager::install(“limma”, version = “3.8”) BiocManager::install(“limma”)

if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”) #BiocManager::install(“Glimma”, version = “3.8”) BiocManager::install(“Glimma”)

this installs other packages I need for this analyses. #http://master.bioconductor.org/packages/release/workflows/html/RNAseq123.html

if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”) #BiocManager::install(“RNAseq123”, version = “3.8”) BiocManager::install(“RNAseq123”)

install.packages(“DataCombine”)

#```

2008 RNAseq DATASET

Import 2008 RNAseq Data for edgeR

Read in the data table - this is the output of the PrepDE.py script run on the ballgown folder, after the from HiSat2-Stringtie pipeline.

Data2008 <- read.delim("Input/2008_gene_count_matrix_ID.txt",row.names="Gene")
dim(Data2008)

[1] 21554    12

head(Data2008)

class(Data2008)

[1] "data.frame"

Make a DGEList object from the data table (MyData)

library(limma)
library(edgeR)
x_2008 <- DGEList(counts=Data2008)
class(x_2008)

[1] "DGEList"
attr(,"package")
[1] "edgeR"

x_2008

An object of class "DGEList"
$counts
             C_L_HS37 C_L_HS38 H_L_HS46 C_M_HS61 C_M_HS62 H_L_HS64 H_L_HS65 H_M_HS66 H_M_HS67
LOC116515328      141       73       57       65       22      109       48       48       43
SRD5A2            416      222      106      153       83      141      137      136       73
SGCD                0        0        0        0        0        0        0        3        0
LOC116520366        0        0        0        0        0        0        0        0        0
SRD5A1             85       16       25       42       13       42       12       20        5
             C_M_HS87 C_L_HS90 H_M_HS93
LOC116515328       51       52       93
SRD5A2             43      100       97
SGCD                0        2        0
LOC116520366        0        0        0
SRD5A1              8       16       12
21549 more rows ...

$samples

7 more rows ...

Add grouping factors.

Treatment, C=Control, H-Heat Stress.
Ecotype, L=Lakeshore (fast-living), M=Meadow (slow-living) These are added in the same order as the samples

Treatment <- as.factor(c("C",   "C",    "H",    "C",    "C",    "H",    "H",    "H",    "H",    "C",    "C", "H"))
Ecotype <- as.factor(c("L", "L", "L", "M", "M", "L", "L", "M", "M", "M", "L", "M"))
group <- (interaction (Treatment, Ecotype))
x_2008 <- DGEList(counts=Data2008,group=group)
#
x_2008$samples

x_2008$samples$lib.size

 [1] 21712713  6917268  6530969  7032355  2852860  7204608  6128116  7202197  5908596  3477156
[11]  5952547  7138686

x_2008

An object of class "DGEList"
$counts
             C_L_HS37 C_L_HS38 H_L_HS46 C_M_HS61 C_M_HS62 H_L_HS64 H_L_HS65 H_M_HS66 H_M_HS67
LOC116515328      141       73       57       65       22      109       48       48       43
SRD5A2            416      222      106      153       83      141      137      136       73
SGCD                0        0        0        0        0        0        0        3        0
LOC116520366        0        0        0        0        0        0        0        0        0
SRD5A1             85       16       25       42       13       42       12       20        5
             C_M_HS87 C_L_HS90 H_M_HS93
LOC116515328       51       52       93
SRD5A2             43      100       97
SGCD                0        2        0
LOC116520366        0        0        0
SRD5A1              8       16       12
21549 more rows ...

$samples

7 more rows ...

Make barplot of the libary sizes

barplot(x_2008$samples$lib.size*1e-6, names=1:12, ylab="Library size (millions)")

Calculate cpm and logcpm

Calculate cpm (counts of reads per million of reads in the library (total number of reads)), and log-counts per million for each library. Then count how many genes have read counts of 0

cpm <- cpm(x_2008)
lcpm <- cpm(x_2008, log=TRUE)
  #count number of gene with no reads mapped (TRUE)
table(rowSums(x_2008$counts==0)==12)

FALSE  TRUE 
17013  4541 

4541 genes have 0 read counts.

Filtering No and Low Expressed Genes

Here, a CPM value of 1 means that a gene is “expressed” if it has at least 20 counts in the sample library size ≈20 million. The last value is the number of individuals that has to have atleast that many counts.

Note that subsetting the entire DGEList-object removes both the counts as well as the associated gene information. For now keep all in so match up during metanalysis

keep.exprs08 <- rowSums(cpm>1)>=3
x_filtered_cpm1_3 <- x_2008[keep.exprs08, keep.lib.sizes=FALSE]
dim(x_filtered_cpm1_3)

[1] 13067    12

x2_2008<-x_filtered_cpm1_3

Normalization the whole dataset with TMM method (edgeR).

x2_2008norm <- calcNormFactors(x2_2008, method = "TMM")
x2_2008norm$samples$norm.factors

 [1] 1.1393092 1.1121736 1.2276980 0.9064604 0.9118162 1.2352898 0.9871102 0.8670515 1.0920831
[10] 0.8620192 0.9264752 0.8434363

x2_2008norm

An object of class "DGEList"
$counts
             C_L_HS37 C_L_HS38 H_L_HS46 C_M_HS61 C_M_HS62 H_L_HS64 H_L_HS65 H_M_HS66 H_M_HS67
LOC116515328      141       73       57       65       22      109       48       48       43
SRD5A2            416      222      106      153       83      141      137      136       73
SRD5A1             85       16       25       42       13       42       12       20        5
DRAP1            3136      285      237      196      226      461      441      201      405
LOC116510667      795       32       41       46       36       41       38       24       18
             C_M_HS87 C_L_HS90 H_M_HS93
LOC116515328       51       52       93
SRD5A2             43      100       97
SRD5A1              8       16       12
DRAP1             294      178      208
LOC116510667       22       33       28
13062 more rows ...

$samples

7 more rows ...

MDS plot

# To create the MDS plots, we assign different colours to the factors of interest. Dimensions 1 and 2 are examined using the colour grouping defined by cell types.
library(RColorBrewer)

Warning: package ‘RColorBrewer’ was built under R version 4.1.2

lcpm <- cpm(x2_2008norm, log=TRUE)
par(mfrow=c(1,1))
col.group <- Treatment
levels(col.group) <- brewer.pal(nlevels(col.group), "Set1")

Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

col.group <- as.character(col.group)
plotMDS(lcpm, labels=Treatment, col=col.group)
title(main="A. Treatments")

lcpm <- cpm(x2_2008norm, log=TRUE)
par(mfrow=c(1,1))

col.group <- Ecotype
levels(col.group) <- brewer.pal(nlevels(col.group), "Set2")

Warning in brewer.pal(nlevels(col.group), "Set2") :
  minimal value for n is 3, returning requested palette with 3 different levels

col.group <- as.character(col.group)
plotMDS(lcpm, labels=Ecotype, col=col.group)
title(main="A. Ecotypes")

x2_2008norm$samples

plotMDS(x2_2008, col = as.numeric(group))

Model: Interaction, Treatment*Ecotype

Here switch to: https://ucdavis-bioinformatics-training.github.io/2018-June-RNA-Seq-Workshop/thursday/DE.html Section 3. Voom transformation and calculation of variance weights

Specify the two-factor model to be fitted. We are specifying that model includes effects for Temperature Treatment, Ecotype, and the Treatment by Ecotype interaction, where each coefficient corresponds to a group mean

Create model matrix: Testing for Interaction between Treatment and Ecotype (~Treatment*Ecotype)

mmI_2008 <- model.matrix(~Treatment*Ecotype)
colnames(mmI_2008)

[1] "(Intercept)"         "TreatmentH"          "EcotypeM"            "TreatmentH:EcotypeM"

Voom transformation

Voom uses variances of the model residuals (observed - fitted). Do Voom transformation of the normalized data.

y_2008 <- voom(x2_2008norm, mmI_2008, plot = F)

Limma, fit linear model

Section 4. Fitting linear models in limma lmFit fits a linear model using weighted least squares for each gene

fit <- lmFit(y_2008, mmI_2008)
head(coef(fit))

             (Intercept) TreatmentH   EcotypeM TreatmentH:EcotypeM
LOC116515328    2.843468  0.3425275  0.6490151          -0.6255152
SRD5A2          4.348671 -0.2956765  0.1387833          -0.2023257
SRD5A1          1.610937  0.1043980  0.6889947          -1.5629723
DRAP1           5.786816 -0.2045245  0.1351220          -0.3470518
LOC116510667    3.743623 -1.3491781 -0.6532750           0.1652129
LOC116510192    3.288866 -0.1486815  0.1684713          -0.9289909

-The coefficient Temperature represents the difference in mean expression between Heat and the control. So a positive value is upregulated in the heat treatment -The coefficient Ecotype represents the difference in mean expression between Meadow and Lakeshore -The coefficient TreatmentH:EcotypeM is the difference between Treatments of the differences between Ecotypes (interaction effect)

Comparisons between groups (log fold-changes) are obtained as contrasts of these fitted linear models: Estimate contrast for each gene.

Empirical Bayes smoothing of standard errors (shrinks standard errors that are much larger or smaller than those from other genes towards the average standard error) (see https://www.degruyter.com/doi/10.2202/1544-6115.1027)

Contrast: Interaction

tmp_I_2008 <- contrasts.fit(fit, coef = 4) #  TreatmentH:EcotypeM
tmp_I_2008 <- eBayes(tmp_I_2008)

What genes are most differentially expressed?

top.table_I_2008 <- topTable(tmp_I_2008, sort.by = "P", n = Inf)
head(top.table_I_2008, 30)

How many DE genes are there?

length(which(top.table_I_2008$adj.P.Val < 0.05))

[1] 0

length(which(top.table_I_2008$adj.P.Val < 0.1))

[1] 0

length(which(top.table_I_2008$P.Value < 0.01)) 

[1] 68

Print the table of results & make MD plot

top.table_I_2008$Gene <- rownames(top.table_I_2008)
top.table_I_2008 <- top.table_I_2008[,c("Gene", names(top.table_I_2008)[1:6])]
write.table(top.table_I_2008, file = "OutputFiles/HS2008_Interaction.txt", row.names = F, sep = "\t", quote = F)
  # Make a MD plot
et_I_2008 <- decideTests(tmp_I_2008) 
summary(et_I_2008)

       TreatmentH:EcotypeM
Down                     0
NotSig               13067
Up                       0

plotMD(tmp_I_2008, column=1, status=et_I_2008[,1], main=colnames(tmp_I_2008)[1], xlim=c(-0.1,20))

While I have the groups split by Treatment and Ecotype, test for effects within those groups

group <- (interaction (Treatment, Ecotype))
mm_2008 <- model.matrix(~0 + group)

# 4. Fitting linear models in limma
# lmFit fits a linear model using weighted least squares for each gene:
fit <- lmFit(y_2008, mm_2008)
head(coef(fit))

             groupC.L groupH.L groupC.M  groupH.M
LOC116515328 2.843468 3.185995 3.492483 3.2094953
SRD5A2       4.348671 4.052994 4.487454 3.9894520
SRD5A1       1.610937 1.715335 2.299931 0.8413571
DRAP1        5.786816 5.582292 5.921938 5.3703621
LOC116510667 3.743623 2.394445 3.090348 1.9063832
LOC116510192 3.288866 3.140184 3.457337 2.3796648

Contrast: Treatment_H-C in Fast-living

H.L = Heat group in Lake (fast living); C.L = Control group in lake (fast-living)

contr <- makeContrasts(groupH.L - groupC.L, levels = colnames(coef(fit)))
tmp_C_Lake08 <- contrasts.fit(fit, contr)
tmp_C_Lake08 <- eBayes(tmp_C_Lake08)
top.table_HC_Lake08<- topTable(tmp_C_Lake08, sort.by = "P", n = Inf)
head(top.table_HC_Lake08, 20)

  # How many significant Genes
length(which(top.table_HC_Lake08$adj.P.Val < 0.05))

[1] 22

length(which(top.table_HC_Lake08$adj.P.Val < 0.1))

[1] 47

length(which(top.table_HC_Lake08$P.Value < 0.01))

[1] 355

  #Print Output File
top.table_HC_Lake08$Gene <- rownames(top.table_HC_Lake08)
top.table_HC_Lake08 <- top.table_HC_Lake08[,c("Gene", names(top.table_HC_Lake08)[1:6])]
write.table(top.table_HC_Lake08, file = "OutputFiles/HS2008_Treatment_H-C in Fast-living.txt", row.names = F, sep = "\t", quote = F)
  # Make MD plot
et_C_Lake08 <- decideTests(tmp_C_Lake08)
summary(et_C_Lake08)

       groupH.L - groupC.L
Down                     1
NotSig               13045
Up                      21

plotMD(tmp_C_Lake08, column=1, status=et_C_Lake08[,1], main=colnames(tmp_C_Lake08)[1], xlim=c(-0.1,20))

Contrast: Treatment H-C in Slow-living

Group H.M = Heat in Meadow, Group C.M = control in Meadow (slow-living)

contr <- makeContrasts(groupH.M - groupC.M, levels = colnames(coef(fit)))
tmp_C_Med08 <- contrasts.fit(fit, contr)
tmp_C_Med08 <- eBayes(tmp_C_Med08)
top.table_HC_Med08<- topTable(tmp_C_Med08, sort.by = "P", n = Inf)
head(top.table_HC_Med08, 20)

length(which(top.table_HC_Med08$adj.P.Val < 0.05))

[1] 7

length(which(top.table_HC_Med08$adj.P.Val < 0.1))

[1] 12

length(which(top.table_HC_Med08$P.Value < 0.01))

[1] 226

top.table_HC_Med08$Gene <- rownames(top.table_HC_Med08)
top.table_HC_Med08 <- top.table_HC_Med08[,c("Gene", names(top.table_HC_Med08)[1:6])]
write.table(top.table_HC_Med08, file = "OutputFiles/HS2008_Treatment_H-C in Slow-living.txt", row.names = F, sep = "\t", quote = F)

et_C_Med08 <- decideTests(tmp_C_Med08)
summary(et_C_Med08)

       groupH.M - groupC.M
Down                     0
NotSig               13060
Up                       7

plotMD(tmp_C_Med08, column=1, status=et_C_Med08[,1], main=colnames(tmp_C_Med08)[1], xlim=c(-0.1,20))

Contrast: Ecotypes_Slow(Meadow)-Fast(Lake) in Control

contr08 <- makeContrasts(groupC.M - groupC.L, levels = colnames(coef(fit)))
  # M = meadow, slow-living  L=lake, fast-living
tmp_C_Ecotypes08 <- contrasts.fit(fit, contr08)
tmp_C_Ecotypes08 <- eBayes(tmp_C_Ecotypes08)
top.table_C_Ecotypes08<- topTable(tmp_C_Ecotypes08, sort.by = "P", n = Inf)
head(top.table_C_Ecotypes08, 20)

length(which(top.table_C_Ecotypes08$adj.P.Val < 0.05))

[1] 0

length(which(top.table_C_Ecotypes08$adj.P.Val < 0.1))

[1] 0

length(which(top.table_C_Ecotypes08$P.Value < 0.01))

[1] 128

top.table_C_Ecotypes08$Gene <- rownames(top.table_C_Ecotypes08)
top.table_C_Ecotypes08 <- top.table_C_Ecotypes08[,c("Gene", names(top.table_C_Ecotypes08)[1:6])]
write.table(top.table_C_Ecotypes08, file = "OutputFiles/HS2008_Ecotypes_S-F within Controls.txt", row.names = F, sep = "\t", quote = F)

et_C_Ecotypes08 <- decideTests(tmp_C_Ecotypes08)
summary(et_C_Ecotypes08)

       groupC.M - groupC.L
Down                     0
NotSig               13067
Up                       0

plotMD(tmp_C_Ecotypes08, column=1, status=et_C_Ecotypes08[,1], main=colnames(tmp_C_Ecotypes08)[1], xlim=c(-8,13))

Contrast: Ecotypes_Slow(Meadow)-Fast(Lake) in Heat

Eheat08 <- makeContrasts(groupH.M - groupH.L, levels = colnames(coef(fit)))
tmp_H_Ecotypes08 <- contrasts.fit(fit, Eheat08)
tmp_H_Ecotypes08 <- eBayes(tmp_H_Ecotypes08)
top.table_H_Ecotypes08<- topTable(tmp_H_Ecotypes08, sort.by = "P", n = Inf)
head(top.table_H_Ecotypes08, 20)

length(which(top.table_H_Ecotypes08$adj.P.Val < 0.05))

[1] 0

length(which(top.table_H_Ecotypes08$adj.P.Val < 0.1))

[1] 0

length(which(top.table_H_Ecotypes08$P.Value < 0.01))

[1] 130

top.table_H_Ecotypes08$Gene <- rownames(top.table_H_Ecotypes08)
top.table_H_Ecotypes08 <- top.table_H_Ecotypes08[,c("Gene", names(top.table_H_Ecotypes08)[1:6])]
write.table(top.table_H_Ecotypes08, file = "OutputFiles/HS2008_Ecotypes_S-F in Heat.txt", row.names = F, sep = "\t", quote = F)

et_H_Ecotypes08 <- decideTests(tmp_H_Ecotypes08)
summary(et_H_Ecotypes08)

       groupH.M - groupH.L
Down                     0
NotSig               13067
Up                       0

plotMD(tmp_H_Ecotypes08, column=1, status=et_H_Ecotypes08[,1], main=colnames(tmp_H_Ecotypes08)[1], xlim=c(-8,13))

Model: Treatment Main Effect

Specifies a model where each coefficient corresponds to a group mean

x2_2008norm ### These are the filtered, normalized data

An object of class "DGEList"
$counts
             C_L_HS37 C_L_HS38 H_L_HS46 C_M_HS61 C_M_HS62 H_L_HS64 H_L_HS65 H_M_HS66 H_M_HS67
LOC116515328      141       73       57       65       22      109       48       48       43
SRD5A2            416      222      106      153       83      141      137      136       73
SRD5A1             85       16       25       42       13       42       12       20        5
DRAP1            3136      285      237      196      226      461      441      201      405
LOC116510667      795       32       41       46       36       41       38       24       18
             C_M_HS87 C_L_HS90 H_M_HS93
LOC116515328       51       52       93
SRD5A2             43      100       97
SRD5A1              8       16       12
DRAP1             294      178      208
LOC116510667       22       33       28
13062 more rows ...

$samples

7 more rows ...

x2_2008norm$samples

x3_2008 <- x2_2008norm
group=Treatment
x3_2008 <- DGEList(x3_2008, group=Treatment)
x3_2008$samples

dim(x3_2008)

[1] 13067    12

x3_2008$samples$norm.factors  ### Why are the norm factors back to one? Redo the normalization again.

 [1] 1 1 1 1 1 1 1 1 1 1 1 1

x3_2008 <- calcNormFactors(x3_2008, method = "TMM")
  ## I need to do this normalization again here because for some reason I lose the normalazion factors when I do the grouping even when I start with x2_2002norm. The normalization factors seem the same as before.
x2_2008norm$samples$norm.factors

 [1] 1.1393092 1.1121736 1.2276980 0.9064604 0.9118162 1.2352898 0.9871102 0.8670515 1.0920831
[10] 0.8620192 0.9264752 0.8434363

x3_2008$samples$norm.factors

 [1] 1.1393092 1.1121736 1.2276980 0.9064604 0.9118162 1.2352898 0.9871102 0.8670515 1.0920831
[10] 0.8620192 0.9264752 0.8434363

  #The normalization factors are the same as before.

#Create the model for effect of heat treatment
mm_heat_2008 <- model.matrix(~0 + group)
mm_heat_2008

   groupC groupH
1       1      0
2       1      0
3       0      1
4       1      0
5       1      0
6       0      1
7       0      1
8       0      1
9       0      1
10      1      0
11      1      0
12      0      1
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"

Voom transformation

yH2008 <- voom(x3_2008, mm_heat_2008, plot = T)

yH2008

An object of class "EList"
$targets

7 more rows ...

$E
             C_L_HS37 C_L_HS38 H_L_HS46 C_M_HS61 C_M_HS62 H_L_HS64 H_L_HS65 H_M_HS66   H_M_HS67
LOC116515328 2.517509 3.257185 2.844129 3.361934 3.113233 3.623132 3.004322 2.958359  2.7544315
SRD5A2       4.075023 4.855175 3.733348 4.590606 5.005084 3.993003 4.507697 4.451203  3.5111604
SRD5A1       1.790703 1.101907 1.671064 2.737902 2.376267 2.257736 1.048265 1.715998 -0.2290803
DRAP1        6.987791 5.214860 4.890422 4.946897 6.444747 5.698531 6.190679 5.013082  5.9750462
LOC116510667 5.008569 2.079881 2.373678 2.867670 3.811204 2.223384 2.671196 1.973156  1.5209414
             C_M_HS87 C_L_HS90 H_M_HS93
LOC116515328 4.103202 3.251734 3.957667
SRD5A2       3.859645 4.188540 4.018103
SRD5A1       1.504164 1.581882 1.054628
DRAP1        6.618825 5.017269 5.114676
LOC116510667 2.908555 2.603578 2.243662
13062 more rows ...

$weights
         [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]      [,8]      [,9]
[1,] 4.291749 2.2042763 2.1799510 1.8310947 0.7488514 2.3907540 1.6372714 1.6941422 1.7525765
[2,] 5.036807 3.7419394 3.3402283 3.4228764 1.6645494 3.5251360 2.7955794 2.8609559 2.9297325
[3,] 2.576807 0.8157236 0.6101544 0.6980242 0.4229410 0.6569032 0.5146054 0.5235510 0.5328918
[4,] 5.181191 4.9364473 4.7564678 4.7951953 3.6015924 4.8449977 4.4555600 4.4939053 4.5335074
[5,] 4.242131 2.1286375 1.0432353 1.7600693 0.7263564 1.1593476 0.8019384 0.8246981 0.8487904
         [,10]     [,11]     [,12]
[1,] 0.8459667 1.5694760 1.6293793
[2,] 1.9307027 3.1545204 2.7866763
[3,] 0.4485882 0.6266538 0.5133820
[4,] 3.8392544 4.6582002 4.4502836
[5,] 0.8183383 1.5070919 0.7988496
13062 more rows ...

$design
  groupC groupH
1      1      0
2      1      0
3      0      1
4      1      0
5      1      0
7 more rows ...

Limma, Fitting linear models

lmFit fits a linear model using weighted least squares for each gene:

fit <- lmFit(yH2008, mm_heat_2008)
head(coef(fit))

               groupC   groupH
LOC116515328 3.049782 3.196429
SRD5A2       4.400828 4.022651
SRD5A1       1.806371 1.300893
DRAP1        5.836818 5.477863
LOC116510667 3.558549 2.173998
LOC116510192 3.339489 2.802578

Comparisons between groups (log fold-changes) are obtained as contrasts of these fitted linear models: ### Contrast: Heat to control

contrH08 <- makeContrasts(groupH - groupC, levels = colnames(coef(fit)))
contrH08

        Contrasts
Levels   groupH - groupC
  groupC              -1
  groupH               1

Estimate contrast for each gene

tmp_Heat08 <- contrasts.fit(fit, contrH08)
tmp_Heat08 <- eBayes(tmp_Heat08)
top.table_Heat08<- topTable(tmp_Heat08, sort.by = "P", n = Inf)
head(top.table_Heat08, 20)

length(which(top.table_Heat08$adj.P.Val < 0.05))

[1] 75

length(which(top.table_Heat08$adj.P.Val < 0.1))

[1] 176

length(which(top.table_Heat08$P.Value < 0.01))

[1] 607

top.table_Heat08$Gene <- rownames(top.table_Heat08)
top.table_Heat08 <- top.table_Heat08[,c("Gene", names(top.table_Heat08)[1:6])]
write.table(top.table_Heat08, file = "Outputfiles/HS2008_Heat.txt", row.names = F, sep = "\t", quote = F)

et_Heat08 <- decideTests(tmp_Heat08)
summary(et_Heat08)

       groupH - groupC
Down                 9
NotSig           12992
Up                  66

plotMD(tmp_Heat08, column=1, status=et_Heat08[,1], main=colnames(tmp_Heat08)[1], xlim=c(-0.1,20))

Model: Ecotype Main Effect

x2_2008norm ## these are the filtered genes

An object of class "DGEList"
$counts
             C_L_HS37 C_L_HS38 H_L_HS46 C_M_HS61 C_M_HS62 H_L_HS64 H_L_HS65 H_M_HS66 H_M_HS67
LOC116515328      141       73       57       65       22      109       48       48       43
SRD5A2            416      222      106      153       83      141      137      136       73
SRD5A1             85       16       25       42       13       42       12       20        5
DRAP1            3136      285      237      196      226      461      441      201      405
LOC116510667      795       32       41       46       36       41       38       24       18
             C_M_HS87 C_L_HS90 H_M_HS93
LOC116515328       51       52       93
SRD5A2             43      100       97
SRD5A1              8       16       12
DRAP1             294      178      208
LOC116510667       22       33       28
13062 more rows ...

$samples

7 more rows ...

x2_2008norm$samples

x4_2008 <- x2_2008norm
group=Ecotype
x4_2008 <- DGEList(x4_2008, group=Ecotype)
x4_2008$samples

dim(x4_2008)

[1] 13067    12

x4_2008 <- calcNormFactors(x4_2008, method = "TMM")
x4_2008$samples$norm.factors

 [1] 1.1393092 1.1121736 1.2276980 0.9064604 0.9118162 1.2352898 0.9871102 0.8670515 1.0920831
[10] 0.8620192 0.9264752 0.8434363

x2_2008norm$samples$norm.factors #check they are the same as before

 [1] 1.1393092 1.1121736 1.2276980 0.9064604 0.9118162 1.2352898 0.9871102 0.8670515 1.0920831
[10] 0.8620192 0.9264752 0.8434363

x4_2008$samples

#Specifies a model where each coefficient corresponds to a group mean
mm_Eco08 <- model.matrix(~0 + group)
mm_Eco08

   groupL groupM
1       1      0
2       1      0
3       1      0
4       0      1
5       0      1
6       1      0
7       1      0
8       0      1
9       0      1
10      0      1
11      1      0
12      0      1
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"

Voom transformation

yE08 <- voom(x4_2008, mm_Eco08, plot = T)

yE08

An object of class "EList"
$targets

7 more rows ...

$E
             C_L_HS37 C_L_HS38 H_L_HS46 C_M_HS61 C_M_HS62 H_L_HS64 H_L_HS65 H_M_HS66   H_M_HS67
LOC116515328 2.517509 3.257185 2.844129 3.361934 3.113233 3.623132 3.004322 2.958359  2.7544315
SRD5A2       4.075023 4.855175 3.733348 4.590606 5.005084 3.993003 4.507697 4.451203  3.5111604
SRD5A1       1.790703 1.101907 1.671064 2.737902 2.376267 2.257736 1.048265 1.715998 -0.2290803
DRAP1        6.987791 5.214860 4.890422 4.946897 6.444747 5.698531 6.190679 5.013082  5.9750462
LOC116510667 5.008569 2.079881 2.373678 2.867670 3.811204 2.223384 2.671196 1.973156  1.5209414
             C_M_HS87 C_L_HS90 H_M_HS93
LOC116515328 4.103202 3.251734 3.957667
SRD5A2       3.859645 4.188540 4.018103
SRD5A1       1.504164 1.581882 1.054628
DRAP1        6.618825 5.017269 5.114676
LOC116510667 2.908555 2.603578 2.243662
13062 more rows ...

$weights
         [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]      [,8]     [,9]
[1,] 3.810152 1.8187265 1.8911088 1.8439018 0.7624255 2.0832877 1.4177390 1.8078138 1.864826
[2,] 4.680753 3.2371313 3.3029773 2.9421672 1.3564361 3.4672462 2.8296471 2.9040197 2.961963
[3,] 2.043539 0.6713276 0.6923766 0.5780549 0.3905078 0.7508484 0.5712766 0.5706236 0.582335
[4,] 5.085720 4.5458687 4.5800450 4.3764437 3.1199796 4.6632880 4.3038389 4.3533659 4.389636
[5,] 3.551354 1.5154205 1.5822625 1.0259959 0.5212574 1.7612047 1.1768030 1.0054562 1.037911
         [,10]     [,11]     [,12]
[1,] 0.8615020 1.2861692 1.7430777
[2,] 1.5761814 2.6644760 2.8388095
[3,] 0.4114858 0.5419531 0.5587457
[4,] 3.3509971 4.1955478 4.3133891
[5,] 0.5635092 1.0716813 0.9707713
13062 more rows ...

$design
  groupL groupM
1      1      0
2      1      0
3      1      0
4      0      1
5      0      1
7 more rows ...

###Limma, Fit linear models lmFit fits a linear model using weighted least squares for each gene:

fit <- lmFit(yE08, mm_Eco08)
head(coef(fit))

               groupL   groupM
LOC116515328 2.996967 3.319712
SRD5A2       4.205797 4.191606
SRD5A1       1.651861 1.476382
DRAP1        5.695491 5.607918
LOC116510667 3.240949 2.401693
LOC116510192 3.221677 2.809600

Contrast: Ecotypes

Comparisons between groups (log fold-changes) are obtained as contrasts of these fitted linear models: Specify which groups to compare: Compare control samples to understand effect of ecotype under control conditions

contr_E08 <- makeContrasts(groupM - groupL, levels = colnames(coef(fit)))
contr_E08

        Contrasts
Levels   groupM - groupL
  groupL              -1
  groupM               1

# Estimate contrast for each gene
tmp_Eco08 <- contrasts.fit(fit, contr_E08)
tmp_Eco08 <- eBayes(tmp_Eco08)
top.table_Eco08<- topTable(tmp_Eco08, sort.by = "P", n = Inf)
head(top.table_Eco08, 20)

length(which(top.table_Eco08$adj.P.Val < 0.05))

[1] 7

length(which(top.table_Eco08$adj.P.Val < 0.1))

[1] 7

length(which(top.table_Eco08$P.Value < 0.01))

[1] 193

top.table_Eco08$Gene <- rownames(top.table_Eco08)
top.table_Eco08 <- top.table_Eco08[,c("Gene", names(top.table_Eco08)[1:6])]
write.table(top.table_Eco08, file = "OutputFiles/HS2008_Ecotype_S-F.txt", row.names = F, sep = "\t", quote = F)

et_Eco08 <- decideTests(tmp_Eco08)
summary(et_Eco08)

       groupM - groupL
Down                 6
NotSig           13060
Up                   1

plotMD(tmp_Eco08, column=1, status=et_Eco08[,1], main=colnames(tmp_Eco08)[1], xlim=c(-8,13))

2012 RNAsea DATASET

Import 2012 RNAseq Data for edgeR

the column sums of the counts. For classic edgeR the data.frame samples must also contain a column group,

### read in your data table - this is the output of the PrepDE.py script run on the ballgown folder (from HiSat2-Stringtie pipeline)
Data2012 <- read.delim("Input/2012_gene_count_matrix_ID.txt",row.names="Gene")
dim(Data2012)

[1] 21554    20

head(Data2012)

class(Data2012)

[1] "data.frame"

Make a DGEList object from the data table (MyData)

library(limma)
library(edgeR)
x_2012 <- DGEList(counts=Data2012)
class(x_2012)

[1] "DGEList"
attr(,"package")
[1] "edgeR"

x_2012

An object of class "DGEList"
$counts
             C_M_HS2012_02 H_L_HS2012_03 C_M_HS2012_05 H_L_HS2012_06 C_L_HS2012_08
LOC116515328           661           641           590           857           450
SRD5A2                 821          1522          3091          2413          2925
SGCD                    13            16            40            19            19
LOC116520366             0             1             0             0             0
SRD5A1                 465           307           180           700           348
             H_L_HS2012_09 C_M_HS2012_11 H_L_HS2012_12 C_M_HS2012_14 H_L_HS2012_15
LOC116515328           665           826           884           943           367
SRD5A2                1820          2028          2464          5027          2001
SGCD                    32             8            39             7             5
LOC116520366             0             0             0             0             0
SRD5A1                 298           633           784           275           182
             H_M_HS2012_18 C_M_HS2012_17 C_L_HS2012_23 C_L_HS2012_26 H_M_HS2012_24
LOC116515328           426           405           445           522           466
SRD5A2                 643           935          1861          1295          1799
SGCD                    10             7             6            18            12
LOC116520366             0             0             6             0             0
SRD5A1                 329           398           320           476           303
             C_L_HS2012_29 H_M_HS2012_30 C_L_HS2012_32 H_M_HS2012_33 H_M_HS2012_35
LOC116515328           473           655           512          1072          1009
SRD5A2                1423          1944          1271          3119          2417
SGCD                    13            18             6            10            16
LOC116520366             0             0             0             0             0
SRD5A1                 470           320           432           642           384
21549 more rows ...

$samples

15 more rows ...

Add grouping factors. Treatment, C=Control, H-Heat Stress. Ecotype, L=Lakeshore, M=Meadow

These are added in the same order as the samples

Treatment <- as.factor(c("C",   "H",    "C",    "H",    "C",    "H",    "C",    "H",    "C",    "H",    "H",    "C",    "C",    "C",    "H",    "C",    "H",    "C",    "H",    "H"))
Ecotype <- as.factor(c("M", "L",    "M",    "L",    "L",    "L",    "M",    "L",    "M",    "L",    "M",    "M",    "L",    "L",    "M",    "L",    "M",    "L",    "M",    "M"))
group <- (interaction (Treatment, Ecotype))
x_2012 <- DGEList(counts=Data2012,group=group)
#
x_2012$samples

x_2012$samples$lib.size

 [1]  72713985  61226930  83885666 101656244  87057796  79520686 106575767 114006502 107344417
[10]  75517067  82607716  69498914  70650368  65395656  61392987  69508224  78191197  57878743
[19] 119997556  85097404

x_2012

An object of class "DGEList"
$counts
             C_M_HS2012_02 H_L_HS2012_03 C_M_HS2012_05 H_L_HS2012_06 C_L_HS2012_08
LOC116515328           661           641           590           857           450
SRD5A2                 821          1522          3091          2413          2925
SGCD                    13            16            40            19            19
LOC116520366             0             1             0             0             0
SRD5A1                 465           307           180           700           348
             H_L_HS2012_09 C_M_HS2012_11 H_L_HS2012_12 C_M_HS2012_14 H_L_HS2012_15
LOC116515328           665           826           884           943           367
SRD5A2                1820          2028          2464          5027          2001
SGCD                    32             8            39             7             5
LOC116520366             0             0             0             0             0
SRD5A1                 298           633           784           275           182
             H_M_HS2012_18 C_M_HS2012_17 C_L_HS2012_23 C_L_HS2012_26 H_M_HS2012_24
LOC116515328           426           405           445           522           466
SRD5A2                 643           935          1861          1295          1799
SGCD                    10             7             6            18            12
LOC116520366             0             0             6             0             0
SRD5A1                 329           398           320           476           303
             C_L_HS2012_29 H_M_HS2012_30 C_L_HS2012_32 H_M_HS2012_33 H_M_HS2012_35
LOC116515328           473           655           512          1072          1009
SRD5A2                1423          1944          1271          3119          2417
SGCD                    13            18             6            10            16
LOC116520366             0             0             0             0             0
SRD5A1                 470           320           432           642           384
21549 more rows ...

$samples

15 more rows ...

Filtering No and Low Expressed Genes

First, make barplot of the libary sizes

barplot(x_2012$samples$lib.size*1e-6, names=1:20, ylab="Library size (millions)")

Calculate cpm and log-cpm

cpm (counts of reads per million of reads in the library (total number of reads)), and log-counts per million for each library. Then count how many genes have read counts of 0

cpm <- cpm(x_2012)
lcpm <- cpm(x_2012, log=TRUE)
table(rowSums(x_2012$counts==0)==12)

FALSE  TRUE 
21378   176 

Filter out lowly expressed genes.

Here, a CPM value of 1 means that a gene is “expressed” if it has at least 20 counts in the sample with library size ≈20 million) or at least 76 counts in the sample with the greatest sequencing depth.

Note that subsetting the entire DGEList-object removes both the counts as well as the associated gene information.

keep.exprs12 <- rowSums(cpm>0.5)>=4
x_filtered_cpm0.5_4 <- x_2012[keep.exprs12, keep.lib.sizes=FALSE]
dim(x_filtered_cpm0.5_4)

[1] 14431    20

x2_2012<-x_filtered_cpm0.5_4

Normalize whole dataset with edgeR using the TMM method.

x2_2012norm <- calcNormFactors(x2_2012, method = "TMM")
x2_2012norm$samples$norm.factors

 [1] 1.0213445 1.0772442 0.8518857 1.0028885 0.9086408 0.9603524 1.0987195 1.0643612 1.1430334
[10] 0.8431775 0.9038269 0.9149328 1.1109291 1.1458121 0.9953395 1.0431701 0.9459050 1.0184398
[19] 1.1535515 0.8905996

x2_2012norm

An object of class "DGEList"
$counts
             C_M_HS2012_02 H_L_HS2012_03 C_M_HS2012_05 H_L_HS2012_06 C_L_HS2012_08
LOC116515328           661           641           590           857           450
SRD5A2                 821          1522          3091          2413          2925
SRD5A1                 465           307           180           700           348
DRAP1                 4930          4054          5802          7227          5423
LOC116510667           520           458           873           712          1004
             H_L_HS2012_09 C_M_HS2012_11 H_L_HS2012_12 C_M_HS2012_14 H_L_HS2012_15
LOC116515328           665           826           884           943           367
SRD5A2                1820          2028          2464          5027          2001
SRD5A1                 298           633           784           275           182
DRAP1                 6020          7385          9620          5775          7026
LOC116510667           644          1254          1017          1292           984
             H_M_HS2012_18 C_M_HS2012_17 C_L_HS2012_23 C_L_HS2012_26 H_M_HS2012_24
LOC116515328           426           405           445           522           466
SRD5A2                 643           935          1861          1295          1799
SRD5A1                 329           398           320           476           303
DRAP1                 5379          5698          8244          4555          6433
LOC116510667           549           624           653           605          1146
             C_L_HS2012_29 H_M_HS2012_30 C_L_HS2012_32 H_M_HS2012_33 H_M_HS2012_35
LOC116515328           473           655           512          1072          1009
SRD5A2                1423          1944          1271          3119          2417
SRD5A1                 470           320           432           642           384
DRAP1                 5902          5896          4096         10202          6004
LOC116510667           521           510           617           979           687
14426 more rows ...

$samples

15 more rows ...

MDS plot

# To create the MDS plots, we assign different colours to the factors of interest. Dimensions 1 and 2 are examined using
# the colour grouping defined by cell types.
library(RColorBrewer)

lcpm <- cpm(x2_2012, log=TRUE)
par(mfrow=c(1,1))
col.group <- Treatment
levels(col.group) <- brewer.pal(nlevels(col.group), "Set1")

Warning in brewer.pal(nlevels(col.group), "Set1") :
  minimal value for n is 3, returning requested palette with 3 different levels

col.group <- as.character(col.group)
plotMDS(lcpm, labels=Treatment, col=col.group)
title(main="A. Treatments")

lcpm <- cpm(x2_2012norm, log=TRUE)
par(mfrow=c(1,1))

col.group <- Ecotype
levels(col.group) <- brewer.pal(nlevels(col.group), "Set2")

Warning in brewer.pal(nlevels(col.group), "Set2") :
  minimal value for n is 3, returning requested palette with 3 different levels

col.group <- as.character(col.group)
plotMDS(lcpm, labels=Ecotype, col=col.group)
title(main="A. Ecotypes")

x2_2012norm$samples

plotMDS(x2_2012norm, col = as.numeric(group))

Here switch to: https://ucdavis-bioinformatics-training.github.io/2018-June-RNA-Seq-Workshop/thursday/DE.html

Section 3. Voom transformation and calculation of variance weights Specify the two-factor model to be fitted. We are specifying that model includes effects for Temperature Treatment, Ecotype, and the Treatment by Ecotype interaction, where each coefficient corresponds to a group mean

Model: Interaction (Treatment * Ecotype

Create model matrix:

mmI_2012 <- model.matrix(~Treatment*Ecotype)
colnames(mmI_2012)

[1] "(Intercept)"         "TreatmentH"          "EcotypeM"            "TreatmentH:EcotypeM"

###Voom Transformation Voom uses variances of the model residuals (observed - fitted)

y_2012 <- voom(x2_2012norm, mmI_2012, plot = F)
y_2012

An object of class "EList"
$targets

15 more rows ...

$E
             C_M_HS2012_02 H_L_HS2012_03 C_M_HS2012_05 H_L_HS2012_06 C_L_HS2012_08
LOC116515328      3.155607      3.282290      3.047172      3.072794      2.510158
SRD5A2            3.468127      4.529211      5.435470      4.565713      5.209242
SRD5A1            2.648647      2.221427      1.337233      2.781043      2.139789
DRAP1             6.053528      5.942292      6.343837      6.148082      6.099783
LOC116510667      2.809764      2.797762      3.612042      2.805548      3.667036
             H_L_HS2012_09 C_M_HS2012_11 H_L_HS2012_12 C_M_HS2012_14 H_L_HS2012_15
LOC116515328      3.123872      2.819957      2.866312      2.943888      2.529390
SRD5A2            4.575697      4.115283      4.344673      5.357634      4.974656
SRD5A1            1.967165      2.436287      2.693224      1.167917      1.519542
DRAP1             6.301245      5.979566      6.309490      5.557739      6.786380
LOC116510667      3.077614      3.421983      3.068407      3.397957      3.951037
             H_M_HS2012_18 C_M_HS2012_17 C_L_HS2012_23 C_L_HS2012_26 H_M_HS2012_24
LOC116515328      2.514764      2.673481      2.505410      2.802444      2.932890
SRD5A2            3.108158      3.879516      4.568378      4.112450      4.880537
SRD5A1            2.142496      2.648358      2.030308      2.669489      2.312709
DRAP1             6.171618      6.486289      6.715344      5.926551      6.718545
LOC116510667      2.880337      3.296480      3.058171      3.015140      4.230177
             C_L_HS2012_29 H_M_HS2012_30 C_L_HS2012_32 H_M_HS2012_33 H_M_HS2012_35
LOC116515328      2.707646      3.148292      3.120633      2.954458      3.735917
SRD5A2            4.295652      4.717024      4.431541      4.494795      4.995792
SRD5A1            2.698476      2.116021      2.875782      2.215249      2.343331
DRAP1             6.347536      6.317483      6.119401      6.204331      6.308320
LOC116510667      2.846949      2.787607      3.389521      2.823598      3.181707
14426 more rows ...

$weights
          [,1]      [,2]      [,3]      [,4]     [,5]     [,6]      [,7]      [,8]      [,9]
[1,]  6.182738  5.903259  6.056009  7.355300 5.939584 6.378359  7.717117  7.938938  7.871353
[2,]  9.422667  9.383102  9.332415 10.117664 9.661865 9.694139 10.151930 10.236822 10.195562
[3,]  4.376471  4.408580  4.275891  5.663460 5.405383 4.805217  5.687775  6.222019  5.833170
[4,] 10.052995 10.026677 10.083372  9.591729 9.897399 9.893988  9.602256  9.372572  9.545282
[5,]  7.057742  6.275500  6.926933  7.747291 7.007658 6.761850  8.536171  8.305682  8.673009
         [,10]    [,11]     [,12]    [,13]    [,14]     [,15]    [,16]    [,17]     [,18]
[1,]  5.793376 6.490866  5.686649 5.914614 5.769836  5.842726 5.666023 6.460017  5.038141
[2,]  9.300680 9.415625  9.043227 9.646473 9.556227  8.913554 9.484961 9.393574  8.973476
[3,]  4.316194 4.720451  3.986462 5.381672 5.243698  4.191888 5.144842 4.694448  4.561177
[4,] 10.057881 9.884544 10.175658 9.905193 9.947590 10.064834 9.976693 9.893783 10.149143
[5,]  6.160236 6.779614  6.534250 6.980825 6.824999  6.117138 6.716084 6.749062  6.032334
         [,19]    [,20]
[1,]  8.518034 6.541315
[2,] 10.245716 9.451541
[3,]  6.628945 4.763026
[4,]  9.146037 9.869577
[5,]  8.768091 6.829556
14426 more rows ...

$design
  (Intercept) TreatmentH EcotypeM TreatmentH:EcotypeM
1           1          0        1                   0
2           1          1        0                   0
3           1          0        1                   0
4           1          1        0                   0
5           1          0        0                   0
15 more rows ...

Limma, Fitting linear models

lmFit fits a linear model using weighted least squares for each gene

fit <- lmFit(y_2012, mmI_2012)
head(coef(fit))

             (Intercept)  TreatmentH    EcotypeM TreatmentH:EcotypeM
LOC116515328    2.716772  0.25938008  0.21041845         -0.13215251
SRD5A2          4.526544  0.06574209 -0.06321462         -0.09321801
SRD5A1          2.466938 -0.17257750 -0.45794156          0.38720492
DRAP1           6.241031  0.05793655 -0.14932446          0.19791652
LOC116510667    3.193839 -0.07537315  0.12125336         -0.09856587
LOC116510192    4.027578  0.09594777  0.15801135         -0.27610125

-The coefficient Temperature represents the difference in mean expression between Heat and the control. So a positive value is upregulated in the heat treatment -The coefficient Ecotype represents the difference in mean expression between Meadow and Lakeshore -The coefficient TreatmentH:EcotypeM is the difference between Treatments of the differences between Ecotypes (interaction effect)

Comparisons between groups (log fold-changes) are obtained as contrasts of these fitted linear models: Estimate contrast for each gene. Empirical Bayes smoothing of standard errors (shrinks standard errors that are much larger or smaller than those from other genes towards the average standard error) (see https://www.degruyter.com/doi/10.2202/1544-6115.1027) ### Contrast: Interaction

tmp_I_2012 <- contrasts.fit(fit, coef = 4) #  TreatmentH:EcotypeM
tmp_I_2012 <- eBayes(tmp_I_2012)

What genes are most differentially expressed?

top.table_I_2012 <- topTable(tmp_I_2012, sort.by = "P", n = Inf)
head(top.table_I_2012, 30)

How many DE genes are there?

length(which(top.table_I_2012$adj.P.Val < 0.05))

[1] 0

length(which(top.table_I_2012$adj.P.Val < 0.1))

[1] 0

length(which(top.table_I_2012$P.Value < 0.01)) 

[1] 72

Print the table of results

top.table_I_2012$Gene <- rownames(top.table_I_2012)
top.table_I_2012 <- top.table_I_2012[,c("Gene", names(top.table_I_2012)[1:6])]
write.table(top.table_I_2012, file = "OutputFiles/HS2012_Interaction.txt", row.names = F, sep = "\t", quote = F)

et_I_2012 <- decideTests(tmp_I_2012) # see if edit to show p.value <0.01
summary(et_I_2012)

       TreatmentH:EcotypeM
Down                     0
NotSig               14431
Up                       0

plotMD(tmp_I_2012, column=1, status=et_I_2012[,1], main=colnames(tmp_I_2012)[1], xlim=c(-0.1,20))

While I have the groups split by Treatment and Ecotype, test for effects withing those groups

group <- (interaction (Treatment, Ecotype))
mm_2012 <- model.matrix(~0 + group)
# 4. Fitting linear models in limma
#lmFit fits a linear model using weighted least squares for each gene:
fit <- lmFit(y_2012, mm_2012)
head(coef(fit))

             groupC.L groupH.L groupC.M groupH.M
LOC116515328 2.716772 2.976152 2.927190 3.054418
SRD5A2       4.526544 4.592286 4.463329 4.435854
SRD5A1       2.466938 2.294360 2.008996 2.223624
DRAP1        6.241031 6.298967 6.091706 6.347559
LOC116510667 3.193839 3.118465 3.315092 3.141153
LOC116510192 4.027578 4.123526 4.185590 4.005436

Contrast: Heat vs Control in Fast-living (Lake) ecotype

contr <- makeContrasts(groupH.L - groupC.L, levels = colnames(coef(fit)))
tmp_C_Lake12 <- contrasts.fit(fit, contr)
tmp_C_Lake12 <- eBayes(tmp_C_Lake12)
top.table_HC_Lake12<- topTable(tmp_C_Lake12, sort.by = "P", n = Inf)
head(top.table_HC_Lake12, 20)

length(which(top.table_HC_Lake12$adj.P.Val < 0.05))

[1] 83

length(which(top.table_HC_Lake12$adj.P.Val < 0.1))

[1] 173

length(which(top.table_HC_Lake12$P.Value < 0.01))

[1] 596

top.table_HC_Lake12$Gene <- rownames(top.table_HC_Lake12)
top.table_HC_Lake12 <- top.table_HC_Lake12[,c("Gene", names(top.table_HC_Lake12)[1:6])]
write.table(top.table_HC_Lake12, file = "OutputFiles/HS2012_Treatment_H-C in Fast-living.txt", row.names = F, sep = "\t", quote = F)

et_C_Lake12 <- decideTests(tmp_C_Lake12)
summary(et_C_Lake12)

       groupH.L - groupC.L
Down                    34
NotSig               14348
Up                      49

plotMD(tmp_C_Lake12, column=1, status=et_C_Lake12[,1], main=colnames(tmp_C_Lake12)[1], xlim=c(-0.1,20))

Contrast: Treatment_H-C in Slow-living (Meadow)

contr <- makeContrasts(groupH.M - groupC.M, levels = colnames(coef(fit)))
tmp_C_Med12 <- contrasts.fit(fit, contr)
tmp_C_Med12 <- eBayes(tmp_C_Med12)
top.table_HC_Med12<- topTable(tmp_C_Med12, sort.by = "P", n = Inf)
head(top.table_HC_Med12, 20)

length(which(top.table_HC_Med12$adj.P.Val < 0.05))

[1] 397

length(which(top.table_HC_Med12$adj.P.Val < 0.1))

[1] 706

length(which(top.table_HC_Med12$P.Value < 0.01))

[1] 1041

top.table_HC_Med12$Gene <- rownames(top.table_HC_Med12)
top.table_HC_Med12 <- top.table_HC_Med12[,c("Gene", names(top.table_HC_Med12)[1:6])]
write.table(top.table_HC_Med12, file = "OutputFiles/HS2012_Treatment_H-C in Slow-living.txt", row.names = F, sep = "\t", quote = F)

et_C_Med12 <- decideTests(tmp_C_Med12)
summary(et_C_Med12)

       groupH.M - groupC.M
Down                   136
NotSig               14034
Up                     261

plotMD(tmp_C_Med12, column=1, status=et_C_Med12[,1], main=colnames(tmp_C_Med12)[1], xlim=c(-0.1,20))

Contrast: Ecotypes_S-F in Controls

contr12 <- makeContrasts(groupC.M - groupC.L, levels = colnames(coef(fit)))
tmp_C_Ecotypes12 <- contrasts.fit(fit, contr12)
tmp_C_Ecotypes12 <- eBayes(tmp_C_Ecotypes12)
top.table_C_Ecotypes12<- topTable(tmp_C_Ecotypes12, sort.by = "P", n = Inf)
head(top.table_C_Ecotypes12, 20)

length(which(top.table_C_Ecotypes12$adj.P.Val < 0.05))

[1] 0

length(which(top.table_C_Ecotypes12$adj.P.Val < 0.1))

[1] 0

length(which(top.table_C_Ecotypes12$P.Value < 0.01))

[1] 111

top.table_C_Ecotypes12$Gene <- rownames(top.table_C_Ecotypes12)
top.table_C_Ecotypes12 <- top.table_C_Ecotypes12[,c("Gene", names(top.table_C_Ecotypes12)[1:6])]
write.table(top.table_C_Ecotypes12, file = "OutputFiles/HS2012_Ecotypes_S-F in Controls.txt", row.names = F, sep = "\t", quote = F)

et_C_Ecotypes12 <- decideTests(tmp_C_Ecotypes12)
summary(et_C_Ecotypes12)

       groupC.M - groupC.L
Down                     0
NotSig               14431
Up                       0

plotMD(tmp_C_Ecotypes12, column=1, status=et_C_Ecotypes12[,1], main=colnames(tmp_C_Ecotypes12)[1], xlim=c(-8,13))

Contrast: Ecotypes_S-F in Heat

Eheat12 <- makeContrasts(groupH.M - groupH.L, levels = colnames(coef(fit)))
tmp_H_Ecotypes12 <- contrasts.fit(fit, Eheat12)
tmp_H_Ecotypes12 <- eBayes(tmp_H_Ecotypes12)
top.table_H_Ecotypes12<- topTable(tmp_H_Ecotypes12, sort.by = "P", n = Inf)
head(top.table_H_Ecotypes12, 20)

length(which(top.table_H_Ecotypes12$adj.P.Val < 0.05))

[1] 0

length(which(top.table_H_Ecotypes12$adj.P.Val < 0.1))

[1] 0

length(which(top.table_H_Ecotypes12$P.Value < 0.01))

[1] 181

top.table_H_Ecotypes12$Gene <- rownames(top.table_H_Ecotypes12)
top.table_H_Ecotypes12 <- top.table_H_Ecotypes12[,c("Gene", names(top.table_H_Ecotypes12)[1:6])]
write.table(top.table_H_Ecotypes12, file = "OutputFiles/HS2012_EcotypesS-F in Heat.txt", row.names = F, sep = "\t", quote = F)

et_H_Ecotypes12 <- decideTests(tmp_H_Ecotypes12)
summary(et_H_Ecotypes12)

       groupH.M - groupH.L
Down                     0
NotSig               14431
Up                       0

plotMD(tmp_H_Ecotypes12, column=1, status=et_C_Ecotypes12[,1], main=colnames(tmp_H_Ecotypes12)[1], xlim=c(-8,13))

Model: Heat as Main Effect

Specifies a model where each coefficient corresponds to a group mean ### Normalization

x2_2012 ## These are the filtered genes

An object of class "DGEList"
$counts
             C_M_HS2012_02 H_L_HS2012_03 C_M_HS2012_05 H_L_HS2012_06 C_L_HS2012_08
LOC116515328           661           641           590           857           450
SRD5A2                 821          1522          3091          2413          2925
SRD5A1                 465           307           180           700           348
DRAP1                 4930          4054          5802          7227          5423
LOC116510667           520           458           873           712          1004
             H_L_HS2012_09 C_M_HS2012_11 H_L_HS2012_12 C_M_HS2012_14 H_L_HS2012_15
LOC116515328           665           826           884           943           367
SRD5A2                1820          2028          2464          5027          2001
SRD5A1                 298           633           784           275           182
DRAP1                 6020          7385          9620          5775          7026
LOC116510667           644          1254          1017          1292           984
             H_M_HS2012_18 C_M_HS2012_17 C_L_HS2012_23 C_L_HS2012_26 H_M_HS2012_24
LOC116515328           426           405           445           522           466
SRD5A2                 643           935          1861          1295          1799
SRD5A1                 329           398           320           476           303
DRAP1                 5379          5698          8244          4555          6433
LOC116510667           549           624           653           605          1146
             C_L_HS2012_29 H_M_HS2012_30 C_L_HS2012_32 H_M_HS2012_33 H_M_HS2012_35
LOC116515328           473           655           512          1072          1009
SRD5A2                1423          1944          1271          3119          2417
SRD5A1                 470           320           432           642           384
DRAP1                 5902          5896          4096         10202          6004
LOC116510667           521           510           617           979           687
14426 more rows ...

$samples

15 more rows ...

x2_2012$samples

x3_2012 <- x2_2012
group=Treatment
x3_2012 <- DGEList(x3_2012, group=Treatment)
#x3_2012$samples$norm
dim(x3_2012)

[1] 14431    20

x3_2012 <- calcNormFactors(x3_2012, method = "TMM")
  ## I need to do this normalization again here because for some reason I lose the normalizion factors when I do the grouping even when I start with x2_2012norm
x3_2012$samples$norm.factors

 [1] 1.0213445 1.0772442 0.8518857 1.0028885 0.9086408 0.9603524 1.0987195 1.0643612 1.1430334
[10] 0.8431775 0.9038269 0.9149328 1.1109291 1.1458121 0.9953395 1.0431701 0.9459050 1.0184398
[19] 1.1535515 0.8905996

x3_2012

An object of class "DGEList"
$counts
             C_M_HS2012_02 H_L_HS2012_03 C_M_HS2012_05 H_L_HS2012_06 C_L_HS2012_08
LOC116515328           661           641           590           857           450
SRD5A2                 821          1522          3091          2413          2925
SRD5A1                 465           307           180           700           348
DRAP1                 4930          4054          5802          7227          5423
LOC116510667           520           458           873           712          1004
             H_L_HS2012_09 C_M_HS2012_11 H_L_HS2012_12 C_M_HS2012_14 H_L_HS2012_15
LOC116515328           665           826           884           943           367
SRD5A2                1820          2028          2464          5027          2001
SRD5A1                 298           633           784           275           182
DRAP1                 6020          7385          9620          5775          7026
LOC116510667           644          1254          1017          1292           984
             H_M_HS2012_18 C_M_HS2012_17 C_L_HS2012_23 C_L_HS2012_26 H_M_HS2012_24
LOC116515328           426           405           445           522           466
SRD5A2                 643           935          1861          1295          1799
SRD5A1                 329           398           320           476           303
DRAP1                 5379          5698          8244          4555          6433
LOC116510667           549           624           653           605          1146
             C_L_HS2012_29 H_M_HS2012_30 C_L_HS2012_32 H_M_HS2012_33 H_M_HS2012_35
LOC116515328           473           655           512          1072          1009
SRD5A2                1423          1944          1271          3119          2417
SRD5A1                 470           320           432           642           384
DRAP1                 5902          5896          4096         10202          6004
LOC116510667           521           510           617           979           687
14426 more rows ...

$samples

15 more rows ...

mm_heat_2012 <- model.matrix(~0 + group)
mm_heat_2012

   groupC groupH
1       1      0
2       0      1
3       1      0
4       0      1
5       1      0
6       0      1
7       1      0
8       0      1
9       1      0
10      0      1
11      0      1
12      1      0
13      1      0
14      1      0
15      0      1
16      1      0
17      0      1
18      1      0
19      0      1
20      0      1
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"

Voom Transformation

yH2012 <- voom(x3_2012, mm_heat_2012, plot = T)

yH2012

An object of class "EList"
$targets

15 more rows ...

$E
             C_M_HS2012_02 H_L_HS2012_03 C_M_HS2012_05 H_L_HS2012_06 C_L_HS2012_08
LOC116515328      3.155607      3.282290      3.047172      3.072794      2.510158
SRD5A2            3.468127      4.529211      5.435470      4.565713      5.209242
SRD5A1            2.648647      2.221427      1.337233      2.781043      2.139789
DRAP1             6.053528      5.942292      6.343837      6.148082      6.099783
LOC116510667      2.809764      2.797762      3.612042      2.805548      3.667036
             H_L_HS2012_09 C_M_HS2012_11 H_L_HS2012_12 C_M_HS2012_14 H_L_HS2012_15
LOC116515328      3.123872      2.819957      2.866312      2.943888      2.529390
SRD5A2            4.575697      4.115283      4.344673      5.357634      4.974656
SRD5A1            1.967165      2.436287      2.693224      1.167917      1.519542
DRAP1             6.301245      5.979566      6.309490      5.557739      6.786380
LOC116510667      3.077614      3.421983      3.068407      3.397957      3.951037
             H_M_HS2012_18 C_M_HS2012_17 C_L_HS2012_23 C_L_HS2012_26 H_M_HS2012_24
LOC116515328      2.514764      2.673481      2.505410      2.802444      2.932890
SRD5A2            3.108158      3.879516      4.568378      4.112450      4.880537
SRD5A1            2.142496      2.648358      2.030308      2.669489      2.312709
DRAP1             6.171618      6.486289      6.715344      5.926551      6.718545
LOC116510667      2.880337      3.296480      3.058171      3.015140      4.230177
             C_L_HS2012_29 H_M_HS2012_30 C_L_HS2012_32 H_M_HS2012_33 H_M_HS2012_35
LOC116515328      2.707646      3.148292      3.120633      2.954458      3.735917
SRD5A2            4.295652      4.717024      4.431541      4.494795      4.995792
SRD5A1            2.698476      2.116021      2.875782      2.215249      2.343331
DRAP1             6.347536      6.317483      6.119401      6.204331      6.308320
LOC116510667      2.846949      2.787607      3.389521      2.823598      3.181707
14426 more rows ...

$weights
          [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]      [,8]      [,9]
[1,]  5.946393  5.983441  5.824033  7.459400  6.155112  6.466385  7.488667  8.051532  7.651402
[2,]  9.562456  9.324487  9.467227 10.176365  9.699676  9.672119 10.291546 10.332829 10.328029
[3,]  4.760082  4.374604  4.651910  5.637254  4.938437  4.772835  6.160439  6.201521  6.313011
[4,] 10.132693 10.140752 10.166672  9.702738 10.077012 10.007773  9.666601  9.482548  9.609091
[5,]  6.925483  6.313405  6.796021  7.813204  7.145608  6.806837  8.459251  8.378047  8.598940
         [,10]     [,11]     [,12]     [,13]     [,14]     [,15]     [,16]     [,17]     [,18]
[1,]  5.870171  6.391097  5.453791  6.128645  5.975255  5.741188  5.869964  6.360462  5.224688
[2,]  9.239769  9.626025  9.175216  9.683625  9.584733  9.123736  9.503124  9.606657  8.960707
[3,]  4.281509  4.708195  4.339903  4.915926  4.785606  4.175347  4.692480  4.681937  4.143705
[4,] 10.171956 10.030112 10.259335 10.083921 10.124826 10.205223 10.153796 10.039314 10.313401
[5,]  6.199075  6.730878  6.399871  7.117744  6.955976  6.061022  6.844654  6.698194  6.151406
         [,19]     [,20]
[1,]  8.471595  6.441199
[2,] 10.388671  9.656744
[3,]  6.638061  4.751194
[4,]  9.302470 10.015203
[5,]  8.776209  6.781626
14426 more rows ...

$design
  groupC groupH
1      1      0
2      0      1
3      1      0
4      0      1
5      1      0
15 more rows ...

Limma, Fit linear model

lmFit fits a linear model using weighted least squares for each gene:

fit <- lmFit(yH2012, mm_heat_2012)
head(coef(fit))

               groupC   groupH
LOC116515328 2.827103 3.014950
SRD5A2       4.494452 4.513861
SRD5A1       2.225403 2.259579
DRAP1        6.167172 6.323219
LOC116510667 3.257236 3.129156
LOC116510192 4.108522 4.064853

Comparisons between groups (log fold-changes) are obtained as contrasts of these fitted linear models: ### Contrast: Treatment, heat to control samples to understand effect of Heat overall

contrH12 <- makeContrasts(groupH - groupC, levels = colnames(coef(fit)))
contrH12

        Contrasts
Levels   groupH - groupC
  groupC              -1
  groupH               1

Estimate contrast for each gene

tmp_Heat12 <- contrasts.fit(fit, contrH12)
tmp_Heat12 <- eBayes(tmp_Heat12)
top.table_Heat12<- topTable(tmp_Heat12, sort.by = "P", n = Inf)
head(top.table_Heat12, 20)

length(which(top.table_Heat12$adj.P.Val < 0.05))

[1] 1101

length(which(top.table_Heat12$adj.P.Val < 0.1))

[1] 1734

length(which(top.table_Heat12$P.Value < 0.01))

[1] 1627

top.table_Heat12$Gene <- rownames(top.table_Heat12)
top.table_Heat12 <- top.table_Heat12[,c("Gene", names(top.table_Heat12)[1:6])]
write.table(top.table_Heat12, file = "OutputFiles/HS2012_Treatment_H-C.txt", row.names = F, sep = "\t", quote = F)

et_Heat12 <- decideTests(tmp_Heat12)
summary(et_Heat12)

       groupH - groupC
Down               430
NotSig           13330
Up                 671

plotMD(tmp_Heat12, column=1, status=et_Heat12[,1], main=colnames(tmp_Heat12)[1], xlim=c(-0.1,20))

Model: Ecotypes as Main Effect

Normalization

x2_2012 ## These are the filtered genes

An object of class "DGEList"
$counts
             C_M_HS2012_02 H_L_HS2012_03 C_M_HS2012_05 H_L_HS2012_06 C_L_HS2012_08
LOC116515328           661           641           590           857           450
SRD5A2                 821          1522          3091          2413          2925
SRD5A1                 465           307           180           700           348
DRAP1                 4930          4054          5802          7227          5423
LOC116510667           520           458           873           712          1004
             H_L_HS2012_09 C_M_HS2012_11 H_L_HS2012_12 C_M_HS2012_14 H_L_HS2012_15
LOC116515328           665           826           884           943           367
SRD5A2                1820          2028          2464          5027          2001
SRD5A1                 298           633           784           275           182
DRAP1                 6020          7385          9620          5775          7026
LOC116510667           644          1254          1017          1292           984
             H_M_HS2012_18 C_M_HS2012_17 C_L_HS2012_23 C_L_HS2012_26 H_M_HS2012_24
LOC116515328           426           405           445           522           466
SRD5A2                 643           935          1861          1295          1799
SRD5A1                 329           398           320           476           303
DRAP1                 5379          5698          8244          4555          6433
LOC116510667           549           624           653           605          1146
             C_L_HS2012_29 H_M_HS2012_30 C_L_HS2012_32 H_M_HS2012_33 H_M_HS2012_35
LOC116515328           473           655           512          1072          1009
SRD5A2                1423          1944          1271          3119          2417
SRD5A1                 470           320           432           642           384
DRAP1                 5902          5896          4096         10202          6004
LOC116510667           521           510           617           979           687
14426 more rows ...

$samples

15 more rows ...

#x2_2012$samples
x4_2012 <- x2_2012norm
group=Ecotype
x4_2012 <- DGEList(x4_2012, group=Ecotype)
#x4_2012$samples$norm.factors
dim(x4_2012)

[1] 14431    20

x4_2012 <- calcNormFactors(x4_2012, method = "TMM")
## I need to do this again here because for some reason I lose the normalization factores
x4_2012$samples$norm.factors

 [1] 1.0213445 1.0772442 0.8518857 1.0028885 0.9086408 0.9603524 1.0987195 1.0643612 1.1430334
[10] 0.8431775 0.9038269 0.9149328 1.1109291 1.1458121 0.9953395 1.0431701 0.9459050 1.0184398
[19] 1.1535515 0.8905996

x4_2012$samples

# Specifies a model where each coefficient corresponds to a group mean
mm_Eco12 <- model.matrix(~0 + group)
mm_Eco12

   groupL groupM
1       0      1
2       1      0
3       0      1
4       1      0
5       1      0
6       1      0
7       0      1
8       1      0
9       0      1
10      1      0
11      0      1
12      0      1
13      1      0
14      1      0
15      0      1
16      1      0
17      0      1
18      1      0
19      0      1
20      0      1
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"

Voom transformation

yE12 <- voom(x4_2012, mm_Eco12, plot = T)

yE12

An object of class "EList"
$targets

15 more rows ...

$E
             C_M_HS2012_02 H_L_HS2012_03 C_M_HS2012_05 H_L_HS2012_06 C_L_HS2012_08
LOC116515328      3.155607      3.282290      3.047172      3.072794      2.510158
SRD5A2            3.468127      4.529211      5.435470      4.565713      5.209242
SRD5A1            2.648647      2.221427      1.337233      2.781043      2.139789
DRAP1             6.053528      5.942292      6.343837      6.148082      6.099783
LOC116510667      2.809764      2.797762      3.612042      2.805548      3.667036
             H_L_HS2012_09 C_M_HS2012_11 H_L_HS2012_12 C_M_HS2012_14 H_L_HS2012_15
LOC116515328      3.123872      2.819957      2.866312      2.943888      2.529390
SRD5A2            4.575697      4.115283      4.344673      5.357634      4.974656
SRD5A1            1.967165      2.436287      2.693224      1.167917      1.519542
DRAP1             6.301245      5.979566      6.309490      5.557739      6.786380
LOC116510667      3.077614      3.421983      3.068407      3.397957      3.951037
             H_M_HS2012_18 C_M_HS2012_17 C_L_HS2012_23 C_L_HS2012_26 H_M_HS2012_24
LOC116515328      2.514764      2.673481      2.505410      2.802444      2.932890
SRD5A2            3.108158      3.879516      4.568378      4.112450      4.880537
SRD5A1            2.142496      2.648358      2.030308      2.669489      2.312709
DRAP1             6.171618      6.486289      6.715344      5.926551      6.718545
LOC116510667      2.880337      3.296480      3.058171      3.015140      4.230177
             C_L_HS2012_29 H_M_HS2012_30 C_L_HS2012_32 H_M_HS2012_33 H_M_HS2012_35
LOC116515328      2.707646      3.148292      3.120633      2.954458      3.735917
SRD5A2            4.295652      4.717024      4.431541      4.494795      4.995792
SRD5A1            2.698476      2.116021      2.875782      2.215249      2.343331
DRAP1             6.347536      6.317483      6.119401      6.204331      6.308320
LOC116510667      2.846949      2.787607      3.389521      2.823598      3.181707
14426 more rows ...

$weights
         [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]     [,9]    [,10]
[1,] 5.650117 5.046856 5.542747 6.287998 5.555038 5.452982 6.983021 6.803350 7.108623 4.949851
[2,] 8.360409 8.275032 8.275213 9.061007 8.652957 8.584194 9.128961 9.238463 9.183534 8.197812
[3,] 4.079015 4.164736 3.989856 5.305232 4.620483 4.528864 5.261172 5.798423 5.390485 4.080680
[4,] 9.260870 9.291703 9.275636 9.091859 9.219210 9.235085 9.025101 8.973765 8.993014 9.303533
[5,] 6.154628 5.658062 6.044031 6.937581 6.184594 6.082405 7.453194 7.409973 7.570941 5.560342
        [,11]    [,12]    [,13]    [,14]    [,15]    [,16]    [,17]    [,18]    [,19]    [,20]
[1,] 5.664943 5.213933 5.532025 5.398561 5.105989 5.306856 5.638847 4.745112 7.435606 5.707608
[2,] 8.372263 8.006207 8.637530 8.547151 7.909490 8.484126 8.351386 8.016747 9.275598 8.406277
[3,] 4.091526 3.724071 4.599808 4.480078 3.638324 4.397981 4.069510 3.901715 5.733115 4.127566
[4,] 9.258832 9.315861 9.222755 9.242685 9.328127 9.255258 9.262423 9.327394 8.894267 9.253009
[5,] 6.170103 5.703869 6.161572 6.027813 5.593147 5.930742 6.142863 5.339271 7.873356 6.214624
14426 more rows ...

$design
  groupL groupM
1      0      1
2      1      0
3      0      1
4      1      0
5      1      0
15 more rows ...

Limma, Fit linear model

lmFit fits a linear model using weighted least squares for each gene:

fit <- lmFit(yE12, mm_Eco12)
head(coef(fit))

               groupL   groupM
LOC116515328 2.851415 2.989817
SRD5A2       4.559231 4.450739
SRD5A1       2.375991 2.114958
DRAP1        6.269677 6.217228
LOC116510667 3.155335 3.230364
LOC116510192 4.076601 4.096342

Comparisons between groups (log fold-changes) are obtained as contrasts of these fitted linear models: Specify which groups to compare: ### Contrast: Ecotype_Slow-living-Fast-living Compare control samples to understand effect of ecotype under control conditions

contr_E12 <- makeContrasts(groupM - groupL, levels = colnames(coef(fit)))
contr_E12

        Contrasts
Levels   groupM - groupL
  groupL              -1
  groupM               1

# Estimate contrast for each gene
tmp_Eco12 <- contrasts.fit(fit, contr_E12)
tmp_Eco12 <- eBayes(tmp_Eco12)
top.table_Eco12<- topTable(tmp_Eco12, sort.by = "P", n = Inf)
head(top.table_Eco12, 20)

length(which(top.table_Eco12$adj.P.Val < 0.05))

[1] 4

length(which(top.table_Eco12$adj.P.Val < 0.1))

[1] 7

length(which(top.table_Eco12$P.Value < 0.01))

[1] 225

top.table_Eco12$Gene <- rownames(top.table_Eco12)
top.table_Eco12 <- top.table_Eco12[,c("Gene", names(top.table_Eco12)[1:6])]
write.table(top.table_Eco12, file = "OutputFiles/HS2012_EcotypeS-F.txt", row.names = F, sep = "\t", quote = F)

et_Eco12 <- decideTests(tmp_Eco12)
summary(et_Eco12)

       groupM - groupL
Down                 1
NotSig           14427
Up                   3

plotMD(tmp_Eco12, column=1, status=et_Eco12[,1], main=colnames(tmp_Eco12)[1], xlim=c(-8,13))

Start the Meta-Analysis

# packages metaRNAseq # Marot et al October 9, 2020

Libraries needed for Meta-analysis library(plyr) library(metaRNASeq) library(data.table) ##### Libraries needed for Upset plot https://cran.r-project.org/web/packages/UpSetR/vignettes/basic.usage.html library(UpSetR) library(ggplot2)

Main Effect of Heat Meta-Analysis

first get the data ready

#Merge top tables results for each dataset - keep the genes in common ~13000
library(plyr)

Warning: package ‘plyr’ was built under R version 4.1.2

top.table_H_intersect <- merge(top.table_Heat08,top.table_Heat12,by="Gene",all.x = FALSE, suffixes = c(".08", ".12"))
dim(top.table_H_intersect)

[1] 12897    13

summary(top.table_H_intersect)

     Gene              logFC.08          AveExpr.08          t.08            P.Value.08    
 Length:12897       Min.   :-6.20550   Min.   :-2.530   Min.   :-7.43821   Min.   :0.0000  
 Class :character   1st Qu.:-0.35556   1st Qu.: 1.378   1st Qu.:-0.89703   1st Qu.:0.1560  
 Mode  :character   Median :-0.04893   Median : 3.343   Median :-0.12647   Median :0.3997  
                    Mean   : 0.05288   Mean   : 3.261   Mean   : 0.04085   Mean   :0.4275  
                    3rd Qu.: 0.36598   3rd Qu.: 4.970   3rd Qu.: 0.82476   3rd Qu.:0.6823  
                    Max.   : 9.81852   Max.   :15.915   Max.   :14.48736   Max.   :1.0000  
  adj.P.Val.08            B.08           logFC.12          AveExpr.12          t.12           
 Min.   :0.0000045   Min.   :-6.775   Min.   :-3.62522   Min.   :-5.122   Min.   :-10.731043  
 1st Qu.:0.6166042   1st Qu.:-6.226   1st Qu.:-0.18604   1st Qu.: 1.965   1st Qu.: -1.050162  
 Median :0.7947920   Median :-5.644   Median :-0.00171   Median : 3.609   Median : -0.009632  
 Mean   :0.7295447   Mean   :-5.383   Mean   : 0.01721   Mean   : 3.543   Mean   :  0.059893  
 3rd Qu.:0.9075900   3rd Qu.:-5.051   3rd Qu.: 0.20145   3rd Qu.: 5.057   3rd Qu.:  1.055380  
 Max.   :0.9999745   Max.   :13.062   Max.   : 9.30789   Max.   :16.730   Max.   : 11.517742  
   P.Value.12       adj.P.Val.12            B.12       
 Min.   :0.00000   Min.   :0.0000015   Min.   :-6.687  
 1st Qu.:0.07103   1st Qu.:0.2893904   1st Qu.:-6.318  
 Median :0.30404   Median :0.6098134   Median :-5.783  
 Mean   :0.36931   Mean   :0.5586179   Mean   :-5.055  
 3rd Qu.:0.63651   3rd Qu.:0.8477064   3rd Qu.:-4.678  
 Max.   :0.99989   Max.   :0.9998884   Max.   :14.083  

head(top.table_H_intersect)

#Put p-value and Fold changes results in lists
H_rawpval <- list("pvalue_08"=top.table_H_intersect[["P.Value.08"]], "pvalue_12"=top.table_H_intersect[["P.Value.12"]])
summary(H_rawpval)

          Length Class  Mode   
pvalue_08 12897  -none- numeric
pvalue_12 12897  -none- numeric

H_adjpval <- list("adjpvalue_08"=top.table_H_intersect[["adj.P.Val.08"]], "adjpvalue_12"=top.table_H_intersect[["adj.P.Val.12"]])
summary(H_adjpval)

             Length Class  Mode   
adjpvalue_08 12897  -none- numeric
adjpvalue_12 12897  -none- numeric

H_FC <- list("H_FC_08"=top.table_H_intersect[["logFC.08"]], "H_FC_12"=top.table_H_intersect[["logFC.12"]])
summary(H_FC)

        Length Class  Mode   
H_FC_08 12897  -none- numeric
H_FC_12 12897  -none- numeric

#make matrix: 1 for genes DE at adjpval of 0.05, and 0 for not.
DE<- mapply(H_adjpval, FUN=function(x) ifelse(x <= 0.05, 1, 0))
studies <-c("2008Dataset", "2012Dataset")
colnames(DE)=paste("DE", studies,sep=".")
rownames(DE)=paste(top.table_H_intersect$"Gene")
#DE<-cbind(top.table_H_intersect$"Gene", DE)
summary(DE)

 DE.2008Dataset     DE.2012Dataset   
 Min.   :0.000000   Min.   :0.00000  
 1st Qu.:0.000000   1st Qu.:0.00000  
 Median :0.000000   Median :0.00000  
 Mean   :0.005815   Mean   :0.08048  
 3rd Qu.:0.000000   3rd Qu.:0.00000  
 Max.   :1.000000   Max.   :1.00000  

head(DE)

      DE.2008Dataset DE.2012Dataset
A1CF               0              1
AAAS               0              0
AACS               0              1
AADAT              0              0
AAGAB              0              0
AAK1               0              0

dim(DE)

[1] 12897     2

#Check for normal distributions
par(mfrow = c(1,2))
hist(H_rawpval[[1]], breaks=100, col="grey", main="2008 Dataset", xlab="Raw p-values")
hist(H_rawpval[[2]], breaks=100, col="grey", main="2012 Dataset", xlab="Raw p-values")

Heat: Calculate the meta-analysis p-value

library(metaRNASeq)
# P-value combination using Fisher method 
fishcomb <- fishercomb(H_rawpval, BHth = 0.05)
summary(fishcomb)

              Length Class  Mode   
DEindices      1155  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric

hist(fishcomb$rawpval, breaks=100, col="grey", main="Fisher Method", xlab = "Raw p-values (meta-analysis)")

# P-value combination using inverse normal method method 
invnormcomb <- invnorm(H_rawpval, nrep=c(12,20), BHth=0.05)
summary(invnormcomb)

              Length Class  Mode   
DEindices      1134  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric

head(invnormcomb)

$DEindices
   [1]     3     7    19    26    37    40    46    51    54    96   122   127   138   139   140
  [16]   145   147   158   162   163   177   209   219   229   235   241   267   286   289   292
  [31]   293   295   338   350   354   358   367   384   405   406   408   418   419   423   431
  [46]   443   455   472   475   485   487   496   500   526   528   534   542   544   545   547
  [61]   552   559   563   566   571   629   645   651   653   659   670   693   701   705   708
  [76]   709   713   719   777   792   794   804   812   823   828   830   840   848   864   889
  [91]   890   893   894   907   927   930   955   963   980   987   997  1002  1019  1027  1044
 [106]  1087  1097  1107  1109  1134  1138  1157  1168  1179  1194  1200  1211  1212  1213  1223
 [121]  1227  1245  1255  1260  1263  1267  1285  1286  1287  1291  1295  1310  1312  1325  1326
 [136]  1334  1335  1384  1385  1388  1389  1428  1433  1439  1443  1474  1488  1492  1504  1522
 [151]  1564  1566  1576  1597  1618  1626  1631  1636  1637  1643  1673  1680  1681  1685  1689
 [166]  1694  1698  1702  1712  1729  1746  1749  1750  1751  1756  1766  1786  1790  1814  1826
 [181]  1840  1842  1848  1849  1855  1876  1879  1880  1884  1889  1894  1895  1900  1919  1957
 [196]  1972  1973  1981  1983  1989  2013  2014  2015  2022  2023  2045  2058  2092  2096  2117
 [211]  2140  2153  2168  2196  2215  2224  2226  2227  2231  2242  2256  2283  2301  2314  2323
 [226]  2336  2337  2340  2346  2368  2371  2378  2385  2390  2395  2404  2449  2453  2458  2459
 [241]  2480  2508  2515  2516  2532  2534  2538  2583  2595  2598  2608  2611  2621  2626  2652
 [256]  2653  2654  2660  2670  2681  2690  2700  2710  2745  2762  2766  2767  2770  2792  2839
 [271]  2841  2843  2848  2870  2885  2889  2893  2894  2913  2914  2920  2931  2946  2951  2979
 [286]  3000  3001  3009  3013  3028  3032  3036  3055  3059  3066  3068  3085  3094  3099  3100
 [301]  3115  3116  3119  3122  3131  3144  3145  3157  3169  3171  3184  3201  3209  3232  3236
 [316]  3252  3253  3255  3264  3266  3277  3328  3336  3342  3365  3367  3370  3385  3391  3402
 [331]  3411  3418  3422  3428  3448  3449  3480  3489  3513  3514  3515  3520  3558  3559  3572
 [346]  3573  3574  3580  3593  3595  3596  3597  3604  3619  3620  3642  3657  3660  3670  3673
 [361]  3697  3704  3705  3706  3707  3708  3712  3716  3718  3723  3746  3747  3748  3784  3789
 [376]  3813  3823  3830  3837  3857  3866  3877  3898  3925  3932  3944  3953  3974  3975  3981
 [391]  3983  3985  3991  3998  4006  4007  4021  4025  4033  4053  4058  4078  4085  4087  4097
 [406]  4099  4105  4116  4117  4119  4129  4131  4153  4156  4162  4163  4177  4192  4198  4199
 [421]  4201  4207  4222  4235  4268  4269  4270  4286  4288  4334  4348  4358  4362  4390  4405
 [436]  4430  4462  4469  4481  4501  4512  4517  4523  4554  4556  4570  4578  4591  4593  4600
 [451]  4622  4623  4660  4673  4716  4728  4764  4776  4816  4819  4822  4823  4829  4834  4835
 [466]  4839  4901  4916  4919  4933  4937  4959  4962  4993  4994  5004  5013  5017  5074  5110
 [481]  5147  5165  5198  5200  5208  5238  5240  5249  5253  5273  5280  5291  5296  5305  5310
 [496]  5322  5333  5335  5350  5353  5355  5378  5402  5403  5414  5415  5427  5433  5444  5467
 [511]  5500  5508  5510  5529  5534  5566  5573  5577  5578  5601  5615  5629  5638  5660  5685
 [526]  5691  5738  5746  5752  5760  5773  5791  5793  5800  5841  5848  5856  5874  5878  5881
 [541]  5887  5901  5903  5912  5918  5927  5967  5972  5977  5987  6016  6031  6042  6072  6073
 [556]  6082  6109  6184  6185  6186  6234  6244  6267  6282  6331  6338  6339  6342  6369  6371
 [571]  6376  6378  6387  6402  6445  6463  6479  6481  6502  6504  6562  6677  6695  6707  6708
 [586]  6751  6761  6773  6787  6788  6794  6798  6801  6804  6808  6809  6816  6877  6887  6892
 [601]  6893  6908  6919  6935  6964  7003  7018  7029  7046  7050  7054  7071  7080  7081  7092
 [616]  7129  7150  7152  7154  7167  7191  7212  7218  7220  7233  7261  7285  7292  7305  7340
 [631]  7378  7383  7404  7412  7463  7477  7495  7496  7501  7508  7516  7522  7529  7532  7543
 [646]  7560  7571  7582  7609  7615  7616  7617  7623  7625  7626  7627  7639  7659  7670  7671
 [661]  7673  7674  7681  7694  7713  7729  7732  7743  7761  7802  7821  7824  7826  7836  7845
 [676]  7860  7862  7863  7872  7890  7894  7905  7917  7920  7938  7977  8001  8003  8026  8069
 [691]  8075  8116  8120  8129  8130  8135  8159  8169  8171  8182  8236  8239  8257  8260  8273
 [706]  8275  8290  8307  8330  8341  8378  8405  8413  8439  8444  8448  8450  8452  8453  8461
 [721]  8491  8496  8502  8517  8527  8534  8546  8561  8582  8594  8600  8608  8615  8636  8647
 [736]  8648  8657  8663  8669  8674  8691  8695  8701  8705  8722  8723  8730  8738  8745  8747
 [751]  8756  8763  8781  8789  8791  8813  8814  8816  8822  8827  8832  8837  8838  8869  8875
 [766]  8909  8914  8952  8958  8959  8962  8983  9007  9013  9014  9031  9043  9051  9056  9067
 [781]  9082  9086  9101  9111  9112  9135  9158  9162  9173  9184  9191  9220  9223  9225  9233
 [796]  9238  9243  9256  9270  9276  9283  9298  9302  9305  9329  9354  9365  9386  9393  9395
 [811]  9415  9421  9424  9425  9433  9434  9435  9499  9532  9537  9558  9565  9578  9586  9644
 [826]  9653  9655  9668  9678  9711  9727  9740  9749  9797  9798  9807  9811  9814  9839  9847
 [841]  9892  9903  9904  9905  9921  9922  9923  9932  9936  9937  9941  9943  9949  9951  9959
 [856]  9963  9977 10001 10008 10013 10017 10033 10042 10049 10062 10063 10066 10069 10070 10092
 [871] 10093 10095 10096 10111 10120 10121 10163 10165 10176 10255 10283 10295 10299 10304 10316
 [886] 10324 10329 10343 10355 10356 10369 10380 10381 10383 10404 10431 10438 10455 10458 10500
 [901] 10504 10515 10519 10562 10564 10573 10575 10576 10590 10591 10592 10604 10605 10623 10631
 [916] 10642 10656 10663 10666 10667 10673 10677 10692 10698 10708 10732 10755 10763 10784 10799
 [931] 10825 10843 10849 10850 10854 10871 10879 10889 10893 10904 10905 10912 10913 10916 10929
 [946] 10936 10963 10964 10974 11019 11022 11032 11071 11078 11093 11094 11095 11107 11119 11121
 [961] 11136 11138 11142 11147 11148 11166 11170 11174 11178 11186 11197 11218 11223 11226 11228
 [976] 11229 11256 11275 11280 11295 11307 11314 11320 11323 11330 11331 11340 11348 11354 11375
 [991] 11381 11393 11398 11411 11413 11434 11435 11476 11481 11503
 [ reached getOption("max.print") -- omitted 134 entries ]

$TestStatistic
   [1]  1.5242672484  1.7262237796  3.5820986228  1.4084765227 -1.2050770939 -0.0336611041
   [7]  3.2309832996 -0.4572145454  2.1551642391 -0.4689016565 -1.3898159008  1.1866253829
  [13] -0.7670811881  0.4815023496  0.5677909112  0.0384470189  1.8619326411 -0.0315418424
  [19]  2.9107726197 -2.8640161332  0.3593292402 -0.9968829460 -0.8500806601 -0.8180861561
  [25]  2.3070349908  2.6697495501  1.3544895361  2.2683501703  1.9296285274  0.7949690644
  [31]  1.0969481286 -0.1254397239  0.7158455622  0.7532596797 -0.8120624336  0.6418425437
  [37]  2.7112967859 -0.6590003850  0.1088571208  3.2431181015  0.9770916198  0.3587429538
  [43]  0.2981832042  0.0627517752  1.0323099601  4.3549834180  2.3669157850 -1.3192965965
  [49]  2.1359915180  1.6948709738  2.8755217351  0.4576200650 -0.0178145859  3.2530414529
  [55] -0.6206782227  1.9738821419 -0.2381043130  1.9764922628 -0.4035902116 -0.4530967806
  [61]  0.4041929956  0.6116462256 -0.7264583120  1.4444553273  0.2360642887  1.7105308488
  [67]  1.5502790206  2.3603372877 -0.9259222202  0.6333501620 -0.1275907823  1.5562163704
  [73]  2.2790889492 -0.7843555827  2.2881623141  1.7038220528 -0.7504809610  2.0705958050
  [79] -0.3880240042  1.1838215494 -0.3809001296 -0.8618648212 -0.4122291910  1.1595707642
  [85]  0.7150712567  0.7042314147  1.9010651662 -1.9744975878  1.3506344855  0.2429863590
  [91] -0.9358462805  1.0672467034 -0.5602922714  0.0090263468 -1.1135616847  3.5505877743
  [97]  0.4805297698  0.1118370863 -0.0994239166 -0.3484555646  1.8597666908  1.2175693751
 [103] -0.3612091797 -0.6791801082  0.6433196158  1.5415494366  1.9297330829 -1.0302019094
 [109] -0.2032479817  0.6631346597  1.4291274627 -0.5129638527  1.6268012880 -0.1707240490
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 [163]  3.5255456418  2.5835027925  0.0420567729  0.7753753308  1.4513411972 -2.5581424893
 [169]  0.2188483501  1.4937134315  1.5931298728 -0.4669381442 -1.6022584314  1.5187803670
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 [457] -0.2689595451  0.3723305051 -0.3134899511  0.2028870134 -0.2075449156  0.4401101218
 [463] -0.1586313438  2.1975185916  0.1466044639  2.3911838391  1.3500673118  0.5434767008
 [469]  2.3277173075  2.3127503419 -0.0004393409  3.3140116467 -2.2704077761  0.7841073465
 [475]  3.4838302222 -1.0761409472  0.1574950180  1.0319876225 -1.3791153817 -0.2750057800
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 [535]  0.1947714274  0.1777210842  1.5484086719  0.1662449625  0.9376290137  0.3307486496
 [541]  1.8279526491  3.1327125146  0.4778766728  2.6998747613  3.4146793468 -0.9017757470
 [547]  3.9812682211  1.6368057914  1.1920519475  1.2234021998 -0.2528851458  3.4702010379
 [553] -1.4835976128 -0.6633393952  0.4711461782  0.5284361852  1.0910468216  0.0380180462
 [559]  2.6781981154 -0.7122032623 -0.4594131247 -0.0003332294  4.1649186037  1.7673506888
 [565]  0.0445133005  4.8304256148  0.9297600548  2.5732460489  1.3698044977  0.6591961085
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 [583]  0.6634017335  1.2364645175  0.2595131370  2.3009236435  0.2882070864 -0.0433417286
 [589]  0.7531188720  1.0308069237  0.5618250708  0.1167565048  0.2094466456 -1.5292592225
 [595]  1.5131755464  1.2665787438  2.0826845388  1.5027150046  1.4667867056  0.2115537977
 [601] -1.3998516818 -0.2206688577 -0.4789751739  1.8346786521  0.0696350381 -0.8389539706
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 [613]  1.3881456224  0.6974564180  0.7904848335  0.7180492628  0.7811756601  0.1645286836
 [619]  0.2934746631 -0.9140745920  0.1272686443  0.4199540448  0.6570100962  0.1377249608
 [625]  0.3580201829 -0.9832963829 -0.1830403628  1.0007701348  3.8176910870  1.9817261986
 [631]  0.0368576191  1.0621912214  1.1258424934 -0.7071107612 -0.3219226067 -0.4109959844
 [637]  1.0106883487  0.4474671276  0.4939577701  2.0698131986  0.0532651487  1.3101278265
 [643]  1.0794041549  0.9675183492  3.7997180261  0.0819448075  0.8567163641 -2.2700793028
 [649] -0.8118648961 -0.1375294226  3.3005126896  2.2033646730  3.1623448616 -0.3829344791
 [655] -0.1529698433  0.9795227443 -0.4459396970  1.1499497426  2.7301016781 -0.3227515385
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 [673] -0.2118105900  0.3096139480  1.1696678518 -1.3173903606 -0.8094567687 -0.1322238652
 [679]  2.2308675381  1.1751752345  2.0064186130  1.2132498719 -0.7017427949  1.3241562809
 [685] -0.3276305243 -0.8595684950  0.0119058746 -1.7937541732  1.5006149303  2.1155659111
 [691]  1.1889708847  1.5333449188  3.2377166055  1.6711639439  0.2231234273  1.0531151748
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 [703]  0.7638253497  1.4081237621  4.2974697081 -1.9762581174 -0.2002644191  2.7885026732
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 [715]  1.8290043926 -2.1534400728 -2.1151926464 -1.6979125761  3.5310387304  1.9969261765
 [721]  0.1125300416  2.0284010554 -0.5557703347 -1.6651429283  1.7860319818  0.3381184979
 [727]  0.8460707088  1.2696344474 -1.2607143310  0.0406926122 -1.5476760476 -0.9655201330
 [733] -0.4245767820  0.0308865207  0.9370031457 -0.4101667035 -1.2504288648 -0.1220622063
 [739] -0.7846119125 -0.1446410957 -0.1918033586 -0.8389943346  0.9362662861 -0.2506121842
 [745] -0.1610191053  0.7638037605  2.0127603043  0.3817602690 -1.7479250559 -0.2702645535
 [751]  1.3150919564 -1.4312668041  1.2704531962  0.1283992711  0.4581235872  0.5303051317
 [757]  0.1036260650  0.1066459844 -0.5766848329  0.9433444173  1.4278253827  0.5176500574
 [763] -1.2190474506 -0.2998867402  0.8007594061 -1.4237171131  0.3390588992  2.0045186677
 [769] -0.4749772205  0.8101337198 -1.0329257500  0.0382935801 -1.2352582174 -0.5487330356
 [775] -1.1729677666  1.2972197963  3.4408450020 -0.7168001549  0.8191197630  0.6083252347
 [781]  1.8589702431  1.1438078193  1.9792033778 -0.9769346334  0.2370785619 -1.9047478259
 [787]  1.2147151207  0.1156806566 -1.5118501586  0.6678364914  0.5032531103  5.5732223518
 [793] -1.0984261027  4.3340053897 -0.9160635794 -0.0355974442 -0.4008850481  0.5272649433
 [799]  0.6548294776  2.4400259021  0.3207878149 -0.5824952581 -0.5716515170  2.7449580175
 [805]  0.6802924811  2.2287874389  1.2564115451  0.4093877681  2.5791083208 -1.3534490419
 [811] -0.5457719797  2.9097739671 -1.0181273670 -0.6840027377 -1.4696869721 -0.3756718505
 [817]  0.2037944373  0.4808951542  0.6697197071  0.9084775426  1.0877626839  0.0590282429
 [823]  6.1484181880  2.4474704019  0.0062971672 -1.1412337674 -0.8103630247  4.4208672393
 [829]  2.3704287472  3.7277468455 -1.2253342106  2.3999035301  0.0957547970 -0.2153972554
 [835] -0.3130896916  1.6549193686  1.4187342660  1.6109581051  0.2565637624  2.7036393462
 [841]  1.3735368611 -1.4600668508  0.4081937780 -1.9159529029  0.6506261406 -1.4627812229
 [847]  0.5741703171  4.3979827502  1.3251556617  0.1935304420  1.8363557604 -2.0757205119
 [853]  0.6577061082  2.5702780211  0.9481516228  1.6115192020 -0.5394512815  0.2943370515
 [859]  2.5922695114  0.0533111227  1.6668387612  0.5842902150 -0.7500208625  2.8292299179
 [865] -0.1054302339  0.0106168860 -0.8493130560 -0.0754458126  1.9209724861  1.0631388103
 [871]  1.3741426294 -0.8277593099  2.5556431287  1.7117704646  0.0926252885  1.8358299360
 [877]  1.3501786953 -0.9851432821 -0.6748523610  1.5760801638 -0.4175799442  0.0846685838
 [883]  1.2955364700  0.6293358078  0.7247025751 -0.9689449935 -1.2644061078  0.2205226919
 [889]  2.7413378943  3.6955137659 -0.1144501974 -1.0151101990  3.2138534221  3.2162946593
 [895]  1.6185230554 -0.2903228953  0.0297226132  0.9278456711  0.3649239523  2.3215164983
 [901] -0.6862582470  0.5513299268  0.5176971206  0.2471658947  0.0060814894  0.1018128491
 [907]  2.8785472860  0.3180617921  1.7505215232  2.0291667546 -0.6167351978  1.7478233519
 [913] -0.8861483925 -1.7181190662  1.2075157248  0.3548287853  2.4922841228  2.4203186876
 [919] -0.1071528072  0.9500491036  0.5490453435  1.2776168230  2.4788603143 -0.8441774876
 [925] -1.9772136646 -0.9565013206  2.8900290930  0.3569595791 -1.0168111060  3.4771100262
 [931] -0.6469965083  0.4299611024  1.4409613799 -0.2084992868  0.3339133494 -1.4747635587
 [937] -1.0782322051  1.0205615915 -0.4071907878 -0.2357199004  0.3754418293 -0.0509781405
 [943]  1.2628641114  0.5569538319  1.5449447017  0.8876633153  1.2139911243  0.6983008989
 [949] -0.4349511110  0.4799247888  2.2815593857  0.7392710591  2.3689346756  1.4092750167
 [955]  3.1427117772  0.2796622670  0.3802129092  2.3816021044 -0.5932314756  0.6900152842
 [961] -0.2803311070  0.9631560161  3.1749236974 -0.9833168642 -0.2898856128 -1.1147423020
 [967] -0.6842575314  1.1790746546  2.1136755358  1.8391766513  1.5493180739 -1.2966553658
 [973] -0.3879951726  1.1506913895  0.1217819161 -0.3752862941  0.0422538119  0.6793507035
 [979]  0.0368792376  5.8761546827 -0.4713675529 -0.7396802114  1.2264478909 -0.8543442355
 [985]  1.9486070060 -0.2607532260  3.0430348686  0.5933067651  0.4993094379  1.2184970095
 [991]  0.9713785116  1.0079542911  1.2686490737  0.0840440063 -0.4458445133 -0.6414205682
 [997]  3.1440465337 -0.7051725248  0.9792141334  0.4407613433
 [ reached getOption("max.print") -- omitted 11897 entries ]

$rawpval
   [1] 6.372098e-02 4.215358e-02 1.704225e-04 7.949501e-02 8.859132e-01 5.134263e-01
   [7] 6.168257e-04 6.762416e-01 1.557449e-02 6.804300e-01 9.177076e-01 1.176877e-01
  [13] 7.784834e-01 3.150798e-01 2.850885e-01 4.846656e-01 3.130629e-02 5.125813e-01
  [19] 1.802681e-03 9.979085e-01 3.596744e-01 8.405893e-01 8.023599e-01 7.933460e-01
  [25] 1.052643e-02 3.795392e-03 8.779012e-02 1.165394e-02 2.682644e-02 2.133157e-01
  [31] 1.363320e-01 5.499123e-01 2.370434e-01 2.256469e-01 7.916221e-01 2.604877e-01
  [37] 3.351030e-03 7.450522e-01 4.566579e-01 5.911460e-04 1.642619e-01 3.598937e-01
  [43] 3.827817e-01 4.749821e-01 1.509635e-01 6.653850e-06 8.968507e-03 9.064650e-01
  [49] 1.634005e-02 4.504995e-02 2.016802e-03 3.236127e-01 5.071066e-01 5.708842e-04
  [55] 7.325943e-01 2.419757e-02 5.940999e-01 2.404953e-02 6.567430e-01 6.747605e-01
  [61] 3.430354e-01 2.703859e-01 7.662211e-01 7.430547e-02 4.066914e-01 4.358388e-02
  [67] 6.053728e-02 9.129163e-03 8.227568e-01 2.632525e-01 5.507636e-01 5.982832e-02
  [73] 1.133089e-02 7.835843e-01 1.106404e-02 4.420717e-02 7.735175e-01 1.919829e-02
  [79] 6.510009e-01 1.182419e-01 6.483613e-01 8.056190e-01 6.599143e-01 1.231118e-01
  [85] 2.372825e-01 2.406443e-01 2.864674e-02 9.758374e-01 8.840627e-02 4.040080e-01
  [91] 8.253238e-01 1.429302e-01 7.123600e-01 4.963991e-01 8.672664e-01 1.921860e-04
  [97] 3.154254e-01 4.554763e-01 5.395992e-01 6.362510e-01 3.145927e-02 1.116938e-01
 [103] 6.410285e-01 7.514881e-01 2.600084e-01 6.159156e-02 2.681996e-02 8.485424e-01
 [109] 5.805294e-01 2.536222e-01 7.648380e-02 6.960117e-01 5.188965e-02 5.677796e-01
 [115] 3.962904e-01 1.220854e-01 6.049490e-01 3.621658e-01 1.008154e-01 5.942160e-01
 [121] 9.333570e-02 3.090952e-03 5.436513e-01 2.838422e-01 7.133015e-01 5.538352e-01
 [127] 7.276699e-08 3.299400e-01 2.706401e-01 3.615771e-01 6.815484e-01 9.972619e-01
 [133] 8.010373e-01 5.501190e-01 9.642490e-01 1.861482e-01 1.486161e-01 1.802297e-03
 [139] 4.266489e-07 8.320357e-05 3.930959e-02 1.839220e-01 3.042381e-01 4.549434e-01
 [145] 2.873100e-04 2.055130e-01 3.458213e-03 2.019956e-01 1.377998e-01 2.737098e-01
 [151] 1.584978e-01 1.475080e-02 3.845842e-01 2.096718e-01 9.629201e-01 1.351204e-01
 [157] 1.945269e-02 9.661516e-04 2.522952e-02 1.615690e-01 4.765974e-02 2.993283e-03
 [163] 2.113057e-04 4.890134e-03 4.832267e-01 2.190590e-01 7.334244e-02 9.947384e-01
 [169] 4.133841e-01 6.762527e-02 5.556553e-02 6.797279e-01 9.454508e-01 6.440889e-02
 [175] 9.795184e-02 7.611742e-01 9.567294e-04 2.170884e-01 6.194392e-01 8.073080e-01
 [181] 1.330479e-01 2.821432e-01 2.933437e-01 3.484380e-01 7.051124e-02 6.937270e-02
 [187] 8.693631e-01 2.787055e-01 5.586691e-02 3.283180e-02 5.556677e-01 4.018148e-01
 [193] 8.563847e-01 9.990040e-01 3.776013e-01 6.359378e-01 6.813274e-01 1.149075e-01
 [199] 3.247637e-01 1.225345e-01 5.884321e-01 5.124997e-01 7.083578e-01 1.349146e-01
 [205] 8.845996e-01 7.113153e-02 7.279550e-01 6.723409e-02 1.737738e-03 1.166104e-01
 [211] 1.488954e-01 7.754837e-01 1.146524e-01 3.680493e-01 5.816688e-01 3.619089e-02
 [217] 6.388287e-01 2.414961e-01 1.615226e-04 6.648847e-02 2.537105e-02 6.218126e-01
 [223] 1.542370e-02 3.021756e-01 7.892124e-01 1.746558e-01 4.970144e-03 3.310645e-01
 [229] 4.399896e-05 8.859470e-02 9.222094e-01 5.436884e-01 4.367992e-01 4.096223e-01
 [235] 2.120701e-03 9.901784e-02 7.703248e-01 2.882434e-01 2.137562e-01 7.402607e-01
 [241] 2.001815e-06 7.198694e-02 1.805338e-01 7.245161e-01 1.001169e-01 4.648107e-01
 [247] 3.475411e-01 1.145928e-01 1.218782e-01 1.699808e-02 4.208051e-01 6.801201e-02
 [253] 9.884912e-01 6.495450e-02 3.241719e-02 2.478115e-02 1.130872e-01 1.511889e-01
 [259] 3.055024e-01 3.376384e-01 5.477809e-01 3.243144e-02 1.922406e-01 2.151904e-01
 [265] 9.875083e-01 4.320749e-01 3.219647e-15 1.193012e-01 8.984464e-01 5.359496e-01
 [271] 9.785882e-01 9.730589e-01 7.929866e-01 2.413098e-01 5.168154e-01 2.983662e-01
 [277] 5.301541e-01 3.581858e-01 1.125578e-01 9.861607e-01 1.962784e-01 8.012029e-01
 [283] 2.264946e-01 7.503360e-01 5.993223e-01 2.105618e-03 3.176148e-01 4.918051e-01
 [289] 8.514124e-04 1.449033e-02 1.062867e-01 1.743050e-14 1.436129e-03 9.249331e-01
 [295] 6.268038e-05 6.849313e-01 8.341622e-01 8.363040e-01 5.908451e-01 3.354999e-01
 [301] 8.956390e-01 7.000822e-01 1.084620e-01 6.009631e-01 2.989749e-01 7.298655e-01
 [307] 7.442733e-01 5.974666e-01 6.922968e-02 1.044061e-01 1.070036e-01 4.264735e-01
 [313] 5.979973e-01 2.383163e-01 6.067959e-01 8.255865e-02 8.701133e-01 4.753193e-02
 [319] 2.260978e-01 3.437801e-01 7.130046e-01 7.611335e-01 7.253378e-01 2.763303e-01
 [325] 8.922214e-01 7.543248e-01 7.152552e-02 9.130082e-01 6.057596e-01 5.212562e-02
 [331] 5.519181e-01 9.652776e-01 4.125651e-01 5.671379e-01 8.223715e-01 5.463136e-01
 [337] 7.934417e-03 9.617870e-04 1.569304e-01 3.133864e-02 1.963282e-01 9.300601e-01
 [343] 6.852974e-01 9.333703e-03 7.840464e-01 7.021989e-01 6.266041e-01 2.862523e-01
 [349] 3.897121e-01 1.227683e-06 1.728854e-01 1.509109e-01 8.851262e-01 5.242808e-04
 [355] 1.053358e-01 3.825513e-01 2.152794e-02 6.678969e-04 7.683914e-01 9.716801e-01
 [361] 3.578687e-01 1.801435e-02 8.786927e-01 8.876069e-02 1.238722e-02 1.030166e-01
 [367] 1.990082e-09 7.529899e-02 1.126558e-01 2.248812e-01 3.917525e-01 4.594884e-01
 [373] 2.616651e-01 1.977900e-02 8.944461e-01 6.011022e-01 5.690647e-01 3.359801e-01
 [379] 5.181418e-02 4.286206e-01 3.709478e-01 7.469801e-01 5.057249e-01 3.813913e-05
 [385] 9.106545e-01 1.932215e-01 4.432043e-03 9.575911e-03 5.339558e-02 1.493380e-01
 [391] 3.050066e-01 6.121571e-01 4.965060e-01 6.258341e-01 3.466085e-01 5.093446e-01
 [397] 3.546281e-01 2.946577e-01 5.276766e-01 3.250595e-01 2.110855e-02 2.824623e-01
 [403] 2.045215e-01 1.395888e-01 1.330797e-04 4.021657e-08 2.422959e-01 2.843540e-04
 [409] 5.612754e-01 3.051494e-01 5.594300e-01 9.010959e-01 9.798125e-01 6.246980e-01
 [415] 3.414445e-02 2.284662e-01 8.897230e-01 9.503732e-05 1.611813e-03 4.474197e-01
 [421] 3.832633e-01 5.553163e-02 2.537024e-03 1.623611e-01 8.085345e-01 1.473105e-02
 [427] 1.529644e-01 9.906479e-01 1.418086e-01 9.143756e-01 2.760442e-03 6.443351e-01
 [433] 3.965146e-02 2.071109e-01 3.976730e-01 5.869937e-02 4.637767e-01 7.443420e-02
 [439] 3.887305e-02 4.211205e-02 8.900656e-01 1.347662e-01 1.094711e-04 7.892575e-01
 [445] 1.867470e-02 7.553590e-01 1.764234e-01 2.957208e-01 3.440191e-02 4.958673e-01
 [451] 2.172852e-01 2.840812e-01 2.814747e-01 5.636695e-01 6.218154e-05 2.063707e-02
 [457] 6.060196e-01 3.548234e-01 6.230458e-01 4.196117e-01 5.822078e-01 3.299287e-01
 [463] 5.630203e-01 1.399172e-02 4.417221e-01 8.397071e-03 8.849720e-02 2.934008e-01
 [469] 9.963560e-03 1.036818e-02 5.001753e-01 4.598384e-04 9.884086e-01 2.164886e-01
 [475] 2.471465e-04 8.590679e-01 4.374274e-01 1.510390e-01 9.160704e-01 6.083441e-01
 [481] 6.301038e-01 8.686705e-01 6.823856e-01 5.248078e-01 1.977080e-03 3.603779e-01
 [487] 3.962699e-03 4.268927e-01 1.907854e-02 6.490893e-01 4.905329e-01 5.986376e-01
 [493] 1.134538e-01 9.573038e-01 2.868581e-01 4.635162e-04 7.960440e-01 4.632648e-01
 [499] 3.722601e-02 1.223007e-03 5.146044e-02 8.991871e-02 8.221937e-01 6.037497e-01
 [505] 6.322335e-02 9.120098e-02 6.850694e-01 4.103976e-02 8.491333e-01 6.635221e-02
 [511] 6.970380e-01 4.450585e-01 9.715032e-01 9.146794e-01 8.081717e-01 6.711291e-01
 [517] 4.931302e-01 7.719920e-01 1.215706e-01 2.600539e-01 2.269876e-02 6.618021e-01
 [523] 2.990924e-02 6.379874e-01 3.764294e-01 8.872928e-05 7.636452e-01 5.256638e-04
 [529] 6.442226e-01 3.212930e-01 1.112673e-01 4.154332e-01 4.669558e-03 2.858140e-05
 [535] 4.227859e-01 4.294710e-01 6.076197e-02 4.339821e-01 1.742175e-01 3.704172e-01
 [541] 3.377833e-02 8.659950e-04 3.163690e-01 3.468279e-03 3.192857e-04 8.164120e-01
 [547] 3.427427e-05 5.083553e-02 1.166204e-01 1.105889e-01 5.998215e-01 2.600345e-04
 [553] 9.310421e-01 7.464434e-01 3.187682e-01 2.985983e-01 1.376261e-01 4.848366e-01
 [559] 3.700971e-03 7.618305e-01 6.770312e-01 5.001329e-01 1.557318e-05 3.858476e-02
 [565] 4.822476e-01 6.812075e-07 1.762477e-01 5.037478e-03 8.537397e-02 2.548849e-01
 [571] 2.434085e-04 5.596668e-02 2.243394e-01 5.875611e-03 1.566563e-01 3.679103e-01
 [577] 2.095141e-02 3.389295e-01 1.250521e-02 2.192357e-01 3.322016e-02 2.795809e-01
 [583] 2.535366e-01 1.081430e-01 3.976197e-01 1.069797e-02 3.865941e-01 5.172854e-01
 [589] 2.256892e-01 1.513157e-01 2.871176e-01 4.535265e-01 4.170498e-01 9.368999e-01
 [595] 6.511754e-02 1.026530e-01 1.863999e-02 6.645628e-02 7.121704e-02 4.162276e-01
 [601] 9.192211e-01 5.873249e-01 6.840219e-01 3.327666e-02 4.722421e-01 7.992524e-01
 [607] 9.545975e-02 2.828238e-01 1.322423e-01 3.833885e-01 5.875286e-01 1.325949e-01
 [613] 8.254635e-02 2.427586e-01 2.146223e-01 2.363635e-01 2.173496e-01 4.346575e-01
 [619] 3.845797e-01 8.196612e-01 4.493639e-01 3.372595e-01 2.555872e-01 4.452289e-01
 [625] 3.601641e-01 8.372692e-01 5.726168e-01 1.584690e-01 6.735322e-05 2.375495e-02
 [631] 4.852993e-01 1.440744e-01 1.301161e-01 7.602512e-01 6.262443e-01 6.594623e-01
 [637] 1.560828e-01 3.272689e-01 3.106680e-01 1.923492e-02 4.787603e-01 9.507630e-02
 [643] 1.402038e-01 1.666425e-01 7.243041e-05 4.673453e-01 1.958008e-01 9.883986e-01
 [649] 7.915654e-01 5.546938e-01 4.825417e-04 1.378453e-02 7.825205e-04 6.491158e-01
 [655] 5.607890e-01 1.636609e-01 6.721796e-01 1.250823e-01 3.165740e-03 6.265583e-01
 [661] 1.129533e-02 2.689284e-01 1.023293e-01 9.131956e-01 9.311996e-02 9.085264e-01
 [667] 5.143573e-01 1.467747e-01 1.338667e-01 2.038564e-03 7.775447e-01 3.507155e-02
 [673] 5.838726e-01 3.784273e-01 1.210673e-01 9.061461e-01 7.908738e-01 5.525964e-01
 [679] 1.284495e-02 1.199623e-01 2.240580e-02 1.125172e-01 7.585802e-01 9.272557e-02
 [685] 6.284045e-01 8.049865e-01 4.952504e-01 9.635738e-01 6.672759e-02 1.719087e-02
 [691] 1.172256e-01 6.259545e-02 6.024522e-04 4.734465e-02 4.117197e-01 1.461441e-01
 [697] 5.410859e-01 6.117280e-01 6.605939e-01 6.399245e-01 4.351779e-03 2.358581e-01
 [703] 2.224857e-01 7.954721e-02 8.637943e-06 9.759372e-01 5.793631e-01 2.647616e-03
 [709] 1.689773e-05 1.896000e-02 7.833393e-02 2.453863e-02 3.089774e-03 3.456505e-01
 [715] 3.369948e-02 9.843579e-01 9.827932e-01 9.552379e-01 2.069656e-04 2.291660e-02
 [721] 4.552016e-01 2.125967e-02 7.108161e-01 9.520579e-01 3.704704e-02 3.676369e-01
 [727] 1.987566e-01 1.021074e-01 8.962941e-01 4.837705e-01 9.391498e-01 8.328578e-01
 [733] 6.644274e-01 4.876800e-01 1.743785e-01 6.591582e-01 8.944285e-01 5.485751e-01
 [739] 7.836594e-01 5.575029e-01 5.760519e-01 7.992638e-01 1.745681e-01 5.989430e-01
 [745] 5.639608e-01 2.224921e-01 2.206993e-02 3.513196e-01 9.597615e-01 6.065216e-01
 [751] 9.423950e-02 9.238231e-01 1.019616e-01 4.489165e-01 3.234318e-01 2.979502e-01
 [757] 4.587331e-01 4.575349e-01 7.179238e-01 1.727524e-01 7.667106e-02 3.023512e-01
 [763] 8.885869e-01 6.178682e-01 2.116355e-01 9.227358e-01 3.672827e-01 2.250726e-02
 [769] 6.825984e-01 2.089317e-01 8.491807e-01 4.847268e-01 8.916328e-01 7.084057e-01
 [775] 8.795956e-01 9.727779e-02 2.899503e-04 7.632513e-01 2.063590e-01 2.714859e-01
 [781] 3.151568e-02 1.263517e-01 2.389656e-02 8.356992e-01 4.062979e-01 9.715936e-01
 [787] 1.122374e-01 4.539528e-01 9.347140e-01 2.521190e-01 3.073932e-01 1.250350e-08
 [793] 8.639908e-01 7.321029e-06 8.201832e-01 5.141983e-01 6.557476e-01 2.990048e-01
 [799] 2.562888e-01 7.343104e-03 3.741856e-01 7.198834e-01 7.162210e-01 3.025932e-03
 [805] 2.481596e-01 1.291403e-02 1.044834e-01 3.411276e-01 4.952786e-03 9.120439e-01
 [811] 7.073887e-01 1.808451e-03 8.456913e-01 7.530133e-01 9.291767e-01 6.464196e-01
 [817] 4.192571e-01 3.152955e-01 2.515182e-01 1.818130e-01 1.383499e-01 4.764648e-01
 [823] 3.912973e-10 7.193147e-03 4.974878e-01 8.731137e-01 7.911342e-01 4.915278e-06
 [829] 8.883734e-03 9.659965e-05 8.897753e-01 8.199697e-03 4.618577e-01 5.852712e-01
 [835] 6.228937e-01 4.897043e-02 7.798825e-02 5.359443e-02 3.987578e-01 3.429234e-03
 [841] 8.479276e-02 9.278641e-01 3.415657e-01 9.723145e-01 2.576439e-01 9.282364e-01
 [847] 2.829263e-01 5.463083e-06 9.255976e-02 4.232718e-01 3.315253e-02 9.810401e-01
 [853] 2.553635e-01 5.080846e-03 1.715261e-01 5.353330e-02 7.052122e-01 3.842502e-01
 [859] 4.767252e-03 4.787420e-01 4.777324e-02 2.795125e-01 7.733789e-01 2.333008e-03
 [865] 5.419828e-01 4.957646e-01 8.021464e-01 5.300700e-01 2.736759e-02 1.438595e-01
 [871] 8.469870e-02 7.960966e-01 5.299588e-03 4.346949e-02 4.631006e-01 3.319141e-02
 [877] 8.847934e-02 8.377232e-01 7.501152e-01 5.750366e-02 6.618729e-01 4.662624e-01
 [883] 9.756761e-02 2.645646e-01 2.343173e-01 8.337137e-01 8.969578e-01 4.127321e-01
 [889] 3.059477e-03 1.097213e-04 5.455595e-01 8.449733e-01 6.548326e-04 6.492871e-04
 [895] 5.277496e-02 6.142154e-01 4.881441e-01 1.767438e-01 3.575841e-01 1.012949e-02
 [901] 7.537249e-01 2.907038e-01 3.023348e-01 4.023899e-01 4.975739e-01 4.594526e-01
 [907] 1.997557e-03 3.752190e-01 4.001418e-02 2.122065e-02 7.312953e-01 4.024731e-02
 [913] 8.122312e-01 9.571126e-01 1.136168e-01 3.613589e-01 6.346223e-03 7.753455e-03
 [919] 5.426661e-01 1.710437e-01 2.914872e-01 1.006923e-01 6.590146e-03 8.007149e-01
 [925] 9.759913e-01 8.305905e-01 1.926031e-03 3.605610e-01 8.453784e-01 2.534249e-04
 [931] 7.411829e-01 3.336120e-01 7.479780e-02 5.825804e-01 3.692225e-01 9.298619e-01
 [937] 8.595349e-01 1.537311e-01 6.580661e-01 5.931750e-01 3.536660e-01 5.203285e-01
 [943] 1.033190e-01 2.887795e-01 6.117982e-02 1.873609e-01 1.123756e-01 2.424945e-01
 [949] 6.682010e-01 3.156404e-01 1.125768e-02 2.298712e-01 8.919702e-03 7.937693e-02
 [955] 8.369529e-04 3.898683e-01 3.518937e-01 8.618756e-03 7.234869e-01 2.450923e-01
 [961] 6.103883e-01 1.677346e-01 7.493795e-04 8.372742e-01 6.140481e-01 8.675196e-01
 [967] 7.530937e-01 1.191842e-01 1.727149e-02 3.294460e-02 6.065264e-02 9.026251e-01
 [973] 6.509902e-01 1.249296e-01 4.515359e-01 6.462762e-01 4.831482e-01 2.484578e-01
 [979] 4.852906e-01 2.099530e-09 6.813109e-01 7.702530e-01 1.100151e-01 8.035429e-01
 [985] 2.567119e-02 6.028586e-01 1.171026e-03 2.764879e-01 3.087807e-01 1.115176e-01
 [991] 1.656799e-01 1.567382e-01 1.022831e-01 4.665107e-01 6.721452e-01 7.393753e-01
 [997] 8.331447e-04 7.596486e-01 1.637371e-01 3.296929e-01
 [ reached getOption("max.print") -- omitted 11897 entries ]

$adjpval
   [1] 2.630632e-01 2.064773e-01 5.258228e-03 2.968925e-01 9.484211e-01 7.613315e-01
   [7] 1.325867e-02 8.482287e-01 1.136584e-01 8.516385e-01 9.625630e-01 3.654752e-01
  [13] 8.961450e-01 6.098764e-01 5.803133e-01 7.428057e-01 1.741445e-01 7.609947e-01
  [19] 2.763425e-02 9.993281e-01 6.507591e-01 9.258759e-01 9.066090e-01 9.030700e-01
  [25] 8.896424e-02 4.503144e-02 3.145955e-01 9.411447e-02 1.576940e-01 5.012998e-01
  [31] 3.957403e-01 7.809953e-01 5.291433e-01 5.150826e-01 9.021428e-01 5.551909e-01
  [37] 4.155612e-02 8.820395e-01 7.229950e-01 1.283503e-02 4.366136e-01 6.507591e-01
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 [241] 2.133670e-04 2.810575e-01 4.587871e-01 8.724635e-01 3.374444e-01 7.281742e-01
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 [253] 9.947387e-01 2.661958e-01 1.775707e-01 1.499026e-01 3.580424e-01 4.175339e-01
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 [979] 7.430392e-01 9.025879e-07 8.522044e-01 8.928593e-01 3.538147e-01 9.074687e-01
 [985] 1.532593e-01 8.099872e-01 2.072598e-02 5.712696e-01 6.035685e-01 3.557795e-01
 [991] 4.388637e-01 4.262869e-01 3.407717e-01 7.290611e-01 8.459624e-01 8.799227e-01
 [997] 1.625577e-02 8.876477e-01 4.361659e-01 6.237420e-01
 [ reached getOption("max.print") -- omitted 11897 entries ]

hist(invnormcomb$rawpval, breaks=100, col="grey", main="Inverse normal method", xlab = "Raw p-values (meta-analysis)")

## Summarize the results. DE are the results from the original analyses of the datasets individually.
# add meta-analysis p-values
H_results_metastats <- data.frame(DE, "DE.fishercomb_adjP"=fishcomb$adjpval, "DE.invnorm_adj_P"=invnormcomb$adjpval)
library(data.table)

Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
data.table 1.14.2 using 1 threads (see ?getDTthreads).  Latest news: r-datatable.com
**********
This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
**********

setDT(H_results_metastats, keep.rownames = "Gene")  # make the rownames a part of the dataframe with column name "Gene"
head(H_results_metastats)

# Make columns that give 1 for significant or 0 for not significant
H_results <- data.frame(DE, "DE.fishercomb"=ifelse(fishcomb$adjpval<=0.05,1,0), "DE.invnorm"=ifelse(invnormcomb$adjpval<=0.05,1,0))
head(H_results)

## Dealing with conflicts in the sign of differential expression 
signsFC <- mapply(H_FC, FUN=function(x) sign(x))
sumsigns <- apply(signsFC,1,sum)
commonsngFC <- ifelse(abs(sumsigns)==dim(signsFC)[2], sign(sumsigns), 0)
  # this produced only 1155 genes
unionDE <- unique(c(fishcomb$DEindices, invnormcomb$DEindicies))
head(unionDE)

[1]  3  7 19 40 46 49

#This keeps only the ones that are significant for either fishcomb or invnormcomb and adds the Signs columns
FC.selecDE <- data.frame(H_results[unionDE,], do.call(cbind,H_FC) [unionDE,], signFC=commonsngFC[unionDE], DE=DE[unionDE])
head(FC.selecDE)

  # This is the subset of genes  that don't have a conflict (1 or -1)
keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),]
  # these are the genes with conflicting signs between HS2008 and HS2012
conflictDE <- FC.selecDE[which(FC.selecDE$signFC==0),]
head(keepDE)

dim(keepDE)

[1] 1055    8

head(conflictDE)

dim(conflictDE)

[1] 100   8

table(FC.selecDE$DE.invnorm)

   0    1 
 128 1027 

table(keepDE$DE.invnorm)

  0   1 
 93 962 

### Merge results and write to  a files
setDT(FC.selecDE, keep.rownames = "Gene")
head(FC.selecDE)

head(H_results_metastats)

# merge top.tables from HS2008 and 2012 - all genes that were not originally filtered otu.
dim(top.table_Heat08)

[1] 13067     7

dim(top.table_Heat12)

[1] 14431     7

top.table.H_All<- merge(top.table_Heat08,top.table_Heat12,by="Gene",all.x = TRUE, all.y=TRUE, suffixes = c(".08", ".12"))
dim(top.table.H_All)

[1] 14601    13

  #merge original results with results from meta-analysis
Heat_results <- merge(top.table.H_All, FC.selecDE, all.x = TRUE)
Heat_results2 <- merge(Heat_results, H_results_metastats, all.x = TRUE)
head(Heat_results2)

write.table(Heat_results2, file = "OutputFiles/Meta_Heat.txt", row.names = F, sep = "\t", quote = F)

Make summary tables

Heat as main effect

## Make a table of results
fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)]
invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)]
indstudy_de <- list(rownames(keepDE)[which(keepDE[,"DE.2008Dataset"]==1)],rownames(keepDE)[which(keepDE[,"DE.2012Dataset"]==1)])
IDD.IRR(fishcomb_de,indstudy_de)

     DE     IDD    Loss     IDR     IRR 
1055.00  265.00    0.00   25.12    0.00 

IDD.IRR(invnorm_de, indstudy_de)

    DE    IDD   Loss    IDR    IRR 
962.00 241.00  69.00  25.05   8.73 

### Venn Diagram  - this was not used; used upset plot instead
#if (require("VennDiagram", quietly = TRUE)) {
#venn.plot<-venn.diagram(x = list(Dataset2008=which(keepDE[,"DE.2008Dataset"]==1), Dataset2012=which(keepDE[,"DE.2012Dataset"]==1),
#invnorm=which(keepDE[,"DE.invnorm"]==1)),
#filename = NULL, col = "black",
#fill = c("blue", "red","green"),
#margin=0.05, alpha = 0.5)
#jpeg("OutputGraphs/venn_Heat.jpg");
#grid.draw(venn.plot);
#dev.off();
#}

Make Upset plot Treatment

https://cran.r-project.org/web/packages/UpSetR/vignettes/basic.usage.html

## upload packages
library(UpSetR)
library(ggplot2)

Warning: package ‘ggplot2’ was built under R version 4.1.2

UpSet Plot

# Heat Treatment 2008
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_Heat08_top <- top.table_Heat08[which(top.table_Heat08$adj.P.Val<=0.05), ]
dim(UP_Heat08_top)

[1] 75  7

  # converting column of "Gene" in dataframe column into vector by passing as index
Treatment_HS2008 = UP_Heat08_top[['Gene']]
# Heat Treatment 2012
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_Heat12_top <- top.table_Heat12[which(top.table_Heat12$adj.P.Val<=0.05), ]
dim(UP_Heat12_top)

[1] 1101    7

  #converting column of "Gene" in dataframe column into vector by passing as index
Treatment_HS2012 = UP_Heat12_top[['Gene']]
# Heat MetaAnalysis
## Keep the genes from the meta-analysis that have invernorm adjusted p-value <= 0.05 and the log fold change is going in the same direction in both analyses
UP_MetaA <- Heat_results2[which(Heat_results2$DE.invnorm_adj_P<=0.05), ]
UP_MetaB <- UP_MetaA[which(abs(UP_MetaA$signFC)==1),]
dim(UP_MetaB)

[1] 962  23

  #converting column of "Gene" in dataframe column into vector by passing as index
Treatment_Meta = UP_MetaB[['Gene']]

listHeat <- list(HS2008 = Treatment_HS2008, HS2012 = Treatment_HS2012, MetaAnalysis = Treatment_Meta)

# Make upset put and save as a .pdf file
pdf("OutputGraphs/Upset_Treatment.pdf")
  #make plot
upset(fromList(listHeat), mainbar.y.label = "Number of Differentially Expressed Genes (adjusted p-value=0.05)", sets.x.label = "DEGs",order.by = "freq")
dev.off()

null device 
          1 

##  Main Effect of Ecotype Meta-Analysis


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI01lcmdlIHRvcCB0YWJsZXMgcmVzdWx0cyBmb3IgZWFjaCBkYXRhc2V0IC0ga2VlcCB0aGUgZ2VuZXMgaW4gY29tbW9uIH4xMzAwMFxubGlicmFyeShwbHlyKVxudG9wLnRhYmxlX0VfaW50ZXJzZWN0IDwtIG1lcmdlKHRvcC50YWJsZV9FY28wOCx0b3AudGFibGVfRWNvMTIsYnk9XCJHZW5lXCIsYWxsLnggPSBGQUxTRSwgc3VmZml4ZXMgPSBjKFwiLjA4XCIsIFwiLjEyXCIpKVxuZGltKHRvcC50YWJsZV9FX2ludGVyc2VjdClcbmBgYCJ9 -->

```r
#Merge top tables results for each dataset - keep the genes in common ~13000
library(plyr)
top.table_E_intersect <- merge(top.table_Eco08,top.table_Eco12,by="Gene",all.x = FALSE, suffixes = c(".08", ".12"))
dim(top.table_E_intersect)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEyODk3ICAgIDEzXG4ifQ== -->

```
[1] 12897    13
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3VtbWFyeSh0b3AudGFibGVfRV9pbnRlcnNlY3QpXG5gYGAifQ== -->

```r
summary(top.table_E_intersect)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin 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 -->

```
     Gene              logFC.08         AveExpr.08          t.08           P.Value.08       
 Length:12897       Min.   :-6.5676   Min.   :-2.530   Min.   :-6.6009   Min.   :0.0000059  
 Class :character   1st Qu.:-0.6835   1st Qu.: 1.378   1st Qu.:-1.1923   1st Qu.:0.1852675  
 Mode  :character   Median :-0.1847   Median : 3.343   Median :-0.4656   Median :0.4155463  
                    Mean   :-0.3412   Mean   : 3.261   Mean   :-0.4216   Mean   :0.4450561  
                    3rd Qu.: 0.1055   3rd Qu.: 4.970   3rd Qu.: 0.3157   3rd Qu.:0.6941145  
                    Max.   : 5.7260   Max.   :15.915   Max.   : 6.7886   Max.   :0.9996759  
  adj.P.Val.08          B.08           logFC.12           AveExpr.12          t.12         
 Min.   :0.02553   Min.   :-6.640   Min.   :-7.547935   Min.   :-5.122   Min.   :-5.68922  
 1st Qu.:0.74172   1st Qu.:-6.160   1st Qu.:-0.113418   1st Qu.: 1.965   1st Qu.:-0.62686  
 Median :0.83024   Median :-5.582   Median : 0.009582   Median : 3.609   Median : 0.05543  
 Mean   :0.83149   Mean   :-5.433   Mean   :-0.002940   Mean   : 3.543   Mean   : 0.04695  
 3rd Qu.:0.92665   3rd Qu.:-4.996   3rd Qu.: 0.127366   3rd Qu.: 5.057   3rd Qu.: 0.71856  
 Max.   :0.99968   Max.   : 3.789   Max.   : 4.356662   Max.   :16.730   Max.   : 7.18485  
   P.Value.12         adj.P.Val.12           B.12       
 Min.   :0.0000004   Min.   :0.005892   Min.   :-6.460  
 1st Qu.:0.2564461   1st Qu.:0.987533   1st Qu.:-6.249  
 Median :0.5090662   Median :0.995089   Median :-5.938  
 Mean   :0.5037875   Mean   :0.969703   Mean   :-5.682  
 3rd Qu.:0.7547713   3rd Qu.:0.995089   3rd Qu.:-5.425  
 Max.   :0.9999532   Max.   :0.999988   Max.   : 6.619  
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGltKHRvcC50YWJsZV9FX2ludGVyc2VjdClcbmBgYCJ9 -->

```r
dim(top.table_E_intersect)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEyODk3ICAgIDEzXG4ifQ== -->

```
[1] 12897    13
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZCh0b3AudGFibGVfRV9pbnRlcnNlY3QpXG5gYGAifQ== -->

```r
head(top.table_E_intersect)
```

<!-- rnb-source-end -->

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</div>

<!-- rnb-frame-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG4jUHV0IHAtdmFsdWUgYW5kIEZvbGQgY2hhbmdlcyByZXN1bHRzIGluIGxpc3RzXG5FX3Jhd3B2YWwgPC0gbGlzdChcInB2YWx1ZV8wOFwiPXRvcC50YWJsZV9FX2ludGVyc2VjdFtbXCJQLlZhbHVlLjA4XCJdXSwgXCJwdmFsdWVfMTJcIj10b3AudGFibGVfRV9pbnRlcnNlY3RbW1wiUC5WYWx1ZS4xMlwiXV0pXG5zdW1tYXJ5KEVfcmF3cHZhbClcbmBgYCJ9 -->

```r

#Put p-value and Fold changes results in lists
E_rawpval <- list("pvalue_08"=top.table_E_intersect[["P.Value.08"]], "pvalue_12"=top.table_E_intersect[["P.Value.12"]])
summary(E_rawpval)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgIExlbmd0aCBDbGFzcyAgTW9kZSAgIFxucHZhbHVlXzA4IDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucHZhbHVlXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->

```
          Length Class  Mode   
pvalue_08 12897  -none- numeric
pvalue_12 12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRV9hZGpwdmFsIDwtIGxpc3QoXCJhZGpwdmFsdWVfMDhcIj10b3AudGFibGVfRV9pbnRlcnNlY3RbW1wiYWRqLlAuVmFsLjA4XCJdXSwgXCJhZGpwdmFsdWVfMTJcIj10b3AudGFibGVfRV9pbnRlcnNlY3RbW1wiYWRqLlAuVmFsLjEyXCJdXSlcbnN1bW1hcnkoRV9hZGpwdmFsKVxuYGBgIn0= -->

```r
E_adjpval <- list("adjpvalue_08"=top.table_E_intersect[["adj.P.Val.08"]], "adjpvalue_12"=top.table_E_intersect[["adj.P.Val.12"]])
summary(E_adjpval)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgIExlbmd0aCBDbGFzcyAgTW9kZSAgIFxuYWRqcHZhbHVlXzA4IDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuYWRqcHZhbHVlXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->

```
             Length Class  Mode   
adjpvalue_08 12897  -none- numeric
adjpvalue_12 12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRV9GQyA8LSBsaXN0KFwiRV9GQ18wOFwiPXRvcC50YWJsZV9FX2ludGVyc2VjdFtbXCJsb2dGQy4wOFwiXV0sIFwiRV9GQ18xMlwiPXRvcC50YWJsZV9FX2ludGVyc2VjdFtbXCJsb2dGQy4xMlwiXV0pXG5zdW1tYXJ5KEVfRkMpXG5gYGAifQ== -->

```r
E_FC <- list("E_FC_08"=top.table_E_intersect[["logFC.08"]], "E_FC_12"=top.table_E_intersect[["logFC.12"]])
summary(E_FC)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkVfRkNfMDggMTI4OTcgIC1ub25lLSBudW1lcmljXG5FX0ZDXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->

```
        Length Class  Mode   
E_FC_08 12897  -none- numeric
E_FC_12 12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI21ha2UgbWF0cml4IG9mIDEgZm9yIGdlbmVzIERFIGFuZCAwIGZvciBub3QuXG5ERTwtIG1hcHBseShFX2FkanB2YWwsIEZVTj1mdW5jdGlvbih4KSBpZmVsc2UoeCA8PSAwLjA1LCAxLCAwKSlcbnN0dWRpZXMgPC1jKFwiMjAwOERhdGFzZXRcIiwgXCIyMDEyRGF0YXNldFwiKVxuY29sbmFtZXMoREUpPXBhc3RlKFwiREVcIiwgc3R1ZGllcyxzZXA9XCIuXCIpXG5yb3duYW1lcyhERSk9cGFzdGUodG9wLnRhYmxlX0VfaW50ZXJzZWN0JFwiR2VuZVwiKVxuI0RFPC1jYmluZCh0b3AudGFibGVfSF9pbnRlcnNlY3QkXCJHZW5lXCIsIERFKVxuc3VtbWFyeShERSlcbmBgYCJ9 -->

```r
#make matrix of 1 for genes DE and 0 for not.
DE<- mapply(E_adjpval, FUN=function(x) ifelse(x <= 0.05, 1, 0))
studies <-c("2008Dataset", "2012Dataset")
colnames(DE)=paste("DE", studies,sep=".")
rownames(DE)=paste(top.table_E_intersect$"Gene")
#DE<-cbind(top.table_H_intersect$"Gene", DE)
summary(DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiIERFLjIwMDhEYXRhc2V0ICAgICAgREUuMjAxMkRhdGFzZXQgICAgIFxuIE1pbi4gICA6MC4wMDAwMDAwICAgTWluLiAgIDowLjAwMDAwMDAgIFxuIDFzdCBRdS46MC4wMDAwMDAwICAgMXN0IFF1LjowLjAwMDAwMDAgIFxuIE1lZGlhbiA6MC4wMDAwMDAwICAgTWVkaWFuIDowLjAwMDAwMDAgIFxuIE1lYW4gICA6MC4wMDA1NDI4ICAgTWVhbiAgIDowLjAwMDMxMDEgIFxuIDNyZCBRdS46MC4wMDAwMDAwICAgM3JkIFF1LjowLjAwMDAwMDAgIFxuIE1heC4gICA6MS4wMDAwMDAwICAgTWF4LiAgIDoxLjAwMDAwMDAgIFxuIn0= -->

```
 DE.2008Dataset      DE.2012Dataset     
 Min.   :0.0000000   Min.   :0.0000000  
 1st Qu.:0.0000000   1st Qu.:0.0000000  
 Median :0.0000000   Median :0.0000000  
 Mean   :0.0005428   Mean   :0.0003101  
 3rd Qu.:0.0000000   3rd Qu.:0.0000000  
 Max.   :1.0000000   Max.   :1.0000000  
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChERSlcbmBgYCJ9 -->

```r
head(DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgREUuMjAwOERhdGFzZXQgREUuMjAxMkRhdGFzZXRcbkExQ0YgICAgICAgICAgICAgICAwICAgICAgICAgICAgICAwXG5BQUFTICAgICAgICAgICAgICAgMCAgICAgICAgICAgICAgMFxuQUFDUyAgICAgICAgICAgICAgIDAgICAgICAgICAgICAgIDBcbkFBREFUICAgICAgICAgICAgICAwICAgICAgICAgICAgICAwXG5BQUdBQiAgICAgICAgICAgICAgMCAgICAgICAgICAgICAgMFxuQUFLMSAgICAgICAgICAgICAgIDAgICAgICAgICAgICAgIDBcbiJ9 -->

```
      DE.2008Dataset DE.2012Dataset
A1CF               0              0
AAAS               0              0
AACS               0              0
AADAT              0              0
AAGAB              0              0
AAK1               0              0
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGltKERFKVxuYGBgIn0= -->

```r
dim(DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEyODk3ICAgICAyXG4ifQ== -->

```
[1] 12897     2
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI0NoZWNrIGZvciBub3JtYWwgZGlzdHJpYnV0aW9uc1xucGFyKG1mcm93ID0gYygxLDIpKVxuaGlzdChFX3Jhd3B2YWxbWzFdXSwgYnJlYWtzPTEwMCwgY29sPVwiZ3JleVwiLCBtYWluPVwiMjAwOCBEYXRhc2V0XCIsIHhsYWI9XCJSYXcgcC12YWx1ZXNcIilcbmhpc3QoRV9yYXdwdmFsW1syXV0sIGJyZWFrcz0xMDAsIGNvbD1cImdyZXlcIiwgbWFpbj1cIjIwMTIgRGF0YXNldFwiLCB4bGFiPVwiUmF3IHAtdmFsdWVzXCIpXG5cbmBgYCJ9 -->

```r
#Check for normal distributions
par(mfrow = c(1,2))
hist(E_rawpval[[1]], breaks=100, col="grey", main="2008 Dataset", xlab="Raw p-values")
hist(E_rawpval[[2]], breaks=100, col="grey", main="2012 Dataset", xlab="Raw p-values")

```

<!-- rnb-source-end -->

<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

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" />

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### Ecotype: Calculate the meta-analysis p-value

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShtZXRhUk5BU2VxKVxuIyBQLXZhbHVlIGNvbWJpbmF0aW9uIHVzaW5nIEZpc2hlciBtZXRob2QgXG5maXNoY29tYiA8LSBmaXNoZXJjb21iKEVfcmF3cHZhbCwgQkh0aCA9IDAuMDUpXG5zdW1tYXJ5KGZpc2hjb21iKVxuYGBgIn0= -->

```r
library(metaRNASeq)
# P-value combination using Fisher method 
fishcomb <- fishercomb(E_rawpval, BHth = 0.05)
summary(fishcomb)
```

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<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkRFaW5kaWNlcyAgICAgICAgMjIgIC1ub25lLSBudW1lcmljXG5UZXN0U3RhdGlzdGljIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucmF3cHZhbCAgICAgICAxMjg5NyAgLW5vbmUtIG51bWVyaWNcbmFkanB2YWwgICAgICAgMTI4OTcgIC1ub25lLSBudW1lcmljXG4ifQ== -->

```
              Length Class  Mode   
DEindices        22  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric
```



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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGlzdChmaXNoY29tYiRyYXdwdmFsLCBicmVha3M9MTAwLCBjb2w9XCJncmV5XCIsIG1haW49XCJGaXNoZXIgTWV0aG9kXCIsIHhsYWIgPSBcIlJhdyBwLXZhbHVlcyBFY290eXBlIChtZXRhLWFuYWx5c2lzKVwiKVxuYGBgIn0= -->

```r
hist(fishcomb$rawpval, breaks=100, col="grey", main="Fisher Method", xlab = "Raw p-values Ecotype (meta-analysis)")
```

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<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBQLXZhbHVlIGNvbWJpbmF0aW9uIHVzaW5nIGludmVyc2Ugbm9tcmFsIG1ldGhvZCBtZXRob2QgXG5pbnZub3JtY29tYiA8LSBpbnZub3JtKEVfcmF3cHZhbCwgbnJlcD1jKDEyLDIwKSwgQkh0aD0wLjA1KVxuc3VtbWFyeShpbnZub3JtY29tYilcbmBgYCJ9 -->

```r
# P-value combination using inverse nomral method method 
invnormcomb <- invnorm(E_rawpval, nrep=c(12,20), BHth=0.05)
summary(invnormcomb)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkRFaW5kaWNlcyAgICAgICAgMTQgIC1ub25lLSBudW1lcmljXG5UZXN0U3RhdGlzdGljIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucmF3cHZhbCAgICAgICAxMjg5NyAgLW5vbmUtIG51bWVyaWNcbmFkanB2YWwgICAgICAgMTI4OTcgIC1ub25lLSBudW1lcmljXG4ifQ== -->

```
              Length Class  Mode   
DEindices        14  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChpbnZub3JtY29tYilcbmBgYCJ9 -->

```r
head(invnormcomb)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin {"data":"$DEindices\n [1]   583  2874  3217  4459  4773  4862  4863  4864  5020  5457  6129  6601  7119 10197\n\n$TestStatistic\n   [1]  2.242554159  0.414884418 -0.718973343  1.533159512  0.080660917 -0.720439972\n   [7]  0.724254738  0.117591844  0.288926933  2.290752879  0.857763511  2.091247791\n  [13] -1.411130605 -0.111580001  0.359459531 -0.030352195 -1.118782961 -0.506511348\n  [19] -0.437108039  2.729763077 -0.151327480 -1.223857364 -0.089192100 -1.181473516\n  [25]  1.119502736 -0.093984416 -0.109818817  0.367327953  0.329236294 -0.373248755\n  [31]  0.268615437  1.282390929  1.650025705 -0.259429046  1.236211499 -0.338529602\n  [37]  0.078337405  1.344131251  0.465052024  0.372790052  1.249608032  0.947490772\n  [43] -0.590261472  1.375582568 -1.837605869  0.399136492  1.143353033 -0.718879490\n  [49] -0.953160701  0.782820964 -0.059791524 -1.332628428 -1.083695080 -2.030681020\n  [55]  1.187261682 -0.255042812  1.259808547  0.119663902  0.032017051  0.836281313\n  [61] -0.307325919 -0.026308483  0.357949329  1.279757601 -0.651264592  0.617911207\n  [67]  0.206402496  1.380507959  1.676192217  2.054855438 -1.970195349  1.206288629\n  [73]  0.785765950  0.258851909  2.260389582  1.369803650 -0.097740741  0.505229152\n  [79] -1.167835704 -2.165008980  0.484632009 -0.680721750  1.968544126  0.671038858\n  [85] -1.481364084  0.047135166 -0.612932046 -0.229526407  0.123717554  0.430482130\n  [91]  1.255867931  1.381800841  1.030757152  1.481811869  1.058309645 -0.974820767\n  [97] -0.906506394 -0.765523013  0.705273271  1.563093954  0.087373539  0.906116050\n [103] -1.916567554 -0.162183871  1.462247078  1.516086297 -0.139117743 -0.639995625\n [109] -0.011395814 -1.124034066  1.365330759 -2.723028217 -0.495343435 -0.318370495\n [115]  0.354277671  0.751493378 -0.081241362  0.449364891  0.462044310  0.215046477\n [121] -1.398684381 -0.868262644  1.251654556 -0.299492083  0.172769903  1.004348480\n [127]  1.068142447 -0.910976198 -0.439315479 -0.729687472  0.436319054 -0.712100305\n [133]  0.445187842 -0.870359748  0.860383460  0.586524296  0.148227361  1.198857948\n [139]  0.080576201  0.169763896  0.277368387 -1.244888499 -0.025994148  0.839040325\n [145]  0.419496872  0.404768248 -0.620785046  0.051722230  1.953916924  0.074635929\n [151] -1.235561090  0.726612049  0.790330539  0.033499224  0.198737614  1.371093559\n [157] -0.012568716  0.338286377 -0.839368863  1.877879228  1.324590404  1.619155389\n [163] -0.977322006 -0.564647795  2.111761107  1.327932147 -1.376301828 -1.467873256\n [169]  1.417818739  1.224787469  0.905817873  1.878325009 -0.575285438  0.525719090\n [175] -1.245904008 -1.060373127  0.251751324  1.447650308  0.785081006  1.086249078\n [181] -0.869949441  1.381835691  0.806794270  1.474932481 -1.081457327  0.775163884\n [187] -1.275595055 -0.286235782 -0.921797673 -0.814550631  0.689386861  0.373570188\n [193]  0.863016228  2.041896126 -0.062682084  0.133494570 -0.333894045  0.406089383\n [199]  0.993485044  0.689562935 -0.017075593 -1.986604868  0.715822700  0.200115647\n [205]  0.047379995 -0.467083415  1.247781560  0.654072522 -0.667253053  1.177493720\n [211]  0.416715815  1.391884365 -0.753434019  0.426545115  0.095590013  0.403848422\n [217] -0.064617416 -0.775950098 -1.408723219 -0.744115887  0.054403555  1.157840319\n [223]  0.063769852 -1.756248471 -1.328252052  1.516089016 -1.254509334  1.227140847\n [229]  0.291481007  0.215571464  0.239800632  0.112442969  0.498069051  1.090010699\n [235] -1.627111454  0.824555543  0.156868659  0.013153113 -0.010061081 -0.475085604\n [241] -1.541548361  1.956702416  2.019716373  0.289036982  0.488020629  0.643625081\n [247] -0.118051143  0.336202084  1.823900091  0.306273521  0.022922564 -0.368682362\n [253] -1.220734728 -0.461561585  1.743336227  0.118280419 -0.301555654  0.480099477\n [259] -0.823314357 -0.836171339  0.762845079 -0.617897512 -0.590701476 -0.580324708\n [265]  0.308659710 -0.664770186 -0.465091358 -0.276994121  1.308680682  3.196888672\n [271]  0.655222573 -0.861706383 -1.186798759 -0.489903441  1.105575749 -2.268024231\n [277]  0.751346463  0.787931023  0.535846547  1.287481181  0.481541148 -1.378989117\n [283] -1.048131828  0.554258282  0.083759059  0.945840969  0.079231091 -1.998164251\n [289] -1.339224868  0.424329407  0.119650695  0.008843832  0.208410851  0.961643933\n [295] -0.657733848 -1.521429982  0.595719801 -0.394797648  0.909513539 -0.048940741\n [301] -0.831088413  0.299830501 -0.660691074  0.242282028 -0.395704094  1.397404248\n [307]  0.783640044  0.167080907 -0.457257962 -0.694898927 -0.636370676  0.125951135\n [313]  2.120678852  1.602079617  0.261797117  0.828187135  0.959516846 -1.348494051\n [319] -0.353146901 -1.797105327 -0.661123955  0.436968218  1.275677959 -0.328569434\n [325]  1.471815039  0.338653983 -0.577483755 -0.473155792 -0.547054174 -1.746711063\n [331]  0.108262725  1.238781765  1.473506870  0.259719708  1.009867388  1.504908408\n [337] -0.371476369  0.105894704 -0.879297007  0.125106559  1.002712606 -0.699050803\n [343] -0.841449382  1.010298298  0.748937330  3.218918452  0.691375329 -1.106679230\n [349]  1.039397656 -0.774600009  0.386298651  0.436218733 -0.181938674  0.276872262\n [355] -0.634066592 -0.114591627 -0.986559481 -0.913753876 -1.921515951 -0.115111874\n [361]  1.501371008 -0.729773309  0.465398951  2.222503870 -1.319686546 -1.759525052\n [367] -0.115483549  1.152156934 -0.210501374  0.868983971  0.026566455  1.755241633\n [373]  1.419328978 -0.636785604  1.520355505  1.440711808  1.218815951  0.693099624\n [379]  1.893054276 -0.562575378  1.844001826  0.417695221  0.042560090 -1.852455400\n [385]  2.006994678  0.810782970 -0.498405229  0.826630226 -0.639838798 -1.945345801\n [391]  1.270919535 -0.578767636  3.024648204  0.833037889  0.791301286 -1.306187837\n [397] -0.395161066 -0.703401984  0.039532854  1.166132572 -0.726559776 -1.221178900\n [403] -0.409921356 -1.200347507 -1.805127045 -1.266736741  0.131203679 -0.871467076\n [409] -0.280587004  0.644118066 -0.683662244 -1.832191614  1.218966818 -0.289468637\n [415]  0.908379986 -0.364978718  0.395323367  0.703354105 -0.877567479  1.218904623\n [421]  0.297435398  0.388433939 -0.187743836  0.064958650  0.994597390  3.130052398\n [427]  0.538360576  0.305798228  1.768774012 -0.440241447 -0.334071367 -0.477136414\n [433]  0.552993525  1.014764878  0.563000768 -1.118945449 -0.546911831  1.159057132\n [439]  1.427737431  0.797897839 -1.493060415 -1.215586120 -2.047110235  1.545371203\n [445] -0.613640660 -0.618791660  0.987923113  1.250200474 -0.354047334  0.775766487\n [451] -0.386517844  0.405325433 -0.113907316  0.194495258 -0.146224420 -0.523290361\n [457]  0.352647695  1.360539259  1.342375177 -0.322184946  1.141169127  0.584695938\n [463] -0.492224894  0.816955673  0.529678988 -1.273017436 -0.436879328  2.067437254\n [469]  0.397206551  0.137987183 -0.239810064  1.086807567 -1.389999580 -0.563988074\n [475] -0.058526187  0.602661493 -1.086147311  0.395034356  0.387905027  0.444096594\n [481]  0.734757025 -0.020798153  1.384328680 -0.273078506  0.330597313  2.154802669\n [487] -0.323220823  0.815846901 -1.540630111  0.554511833  1.000006883  0.221735773\n [493] -0.704757645  0.266127770 -0.154633523  0.433250205  0.906923960  0.017166998\n [499] -1.256048345 -0.699816640 -0.514064357  0.021734626 -0.220879113  0.893349660\n [505] -0.319147384  0.645359979  0.235882687 -0.551936223  0.297667702  0.211990074\n [511]  2.008174499  1.075330160  0.289110907  0.496367698  0.733620164 -1.039998982\n [517]  1.725060673 -1.566907040 -0.844393882 -1.754865106 -0.825913735 -1.269754470\n [523] -0.626912371  0.295392987 -0.849061679 -0.630726596 -0.017982387 -0.399313316\n [529] -0.202029049 -0.260985266 -0.755375165 -1.074626883  0.605562418  1.366350762\n [535]  0.037791621 -0.476955060  2.097858848 -0.143016967  0.498358820  1.040000433\n [541]  0.632616566 -1.035160550 -0.648445192  1.221192501  0.123864338  1.119007245\n [547] -1.142537034 -1.048766907  0.736490132 -0.118882667  0.409754676  0.323881887\n [553]  1.766428065  0.374058456 -1.846172618  0.824362510 -0.773977903  0.092039395\n [559] -1.242484436  1.513967871  1.487392989  1.210992026 -0.506214603  0.554391162\n [565] -1.029077897 -2.483568408  0.040811298 -1.814523116 -0.217685244  2.109173047\n [571] -0.140047156 -0.158996856  1.500145216  0.703203615  0.961759847 -0.223496873\n [577] -2.227152100  0.010996819  0.044985272 -0.200539204  0.671874725  0.150376483\n [583]  4.217106879  0.405799241 -0.326034277 -0.301334750 -1.570151233 -0.731888139\n [589]  0.448321748  0.288994905  0.170897902 -0.548587138 -0.572266167  1.039318881\n [595]  0.277876028 -0.418097969 -1.340332692  0.008701103  0.374047857 -0.858814125\n [601]  1.250128745  0.463739874  1.312970406 -1.624951230 -0.966967169  0.352602700\n [607] -1.024188759  3.853207731  0.558579582 -0.491657360 -0.804403989 -1.760179000\n [613]  1.084537797  0.217830935  1.209214321 -1.500325967 -1.003723458  1.207633364\n [619]  0.671759043  0.842293475  0.280275410 -1.225022151 -0.647272923 -0.273157403\n [625]  1.928613007  0.650284012  0.484008192  0.894676720  1.172692876  0.155150631\n [631] -1.755305869  0.251756490  0.370921885 -0.254739995  1.068099504  0.993975775\n [637] -1.416573602  0.485805520  0.186645164 -0.574680194 -0.640825444 -2.024833927\n [643] -1.175653017  0.836079598 -0.717746032 -0.163115914  0.109011874 -0.629141519\n [649] -1.143342464  0.126820938  0.152134858 -2.314868038  0.278955172  2.159231600\n [655]  1.533314260  0.141896834 -0.596193420 -0.371543612 -0.970937171 -0.324006916\n [661] -1.705097223  0.578777279  1.524331113  0.843292120  0.744943234 -1.255578607\n [667] -1.104291729 -0.799736256  0.212793147 -1.605994778  0.073043387  1.208535238\n [673] -0.791676765  2.061006875 -0.509795489  0.931069228 -0.855333825 -0.793034118\n [679] -0.454931609 -0.700212170 -1.645494352 -1.151940540 -0.902508695 -0.154977808\n [685] -0.598583325  0.602421964 -0.628070467 -1.184889977 -1.714551766 -1.089723411\n [691] -1.454277342 -1.641245949  0.251481837 -1.801505541  0.028972061 -0.794705764\n [697] -0.212440944  0.911647707 -1.548562095  0.273505823  1.647997963  0.416511411\n [703]  0.638252679 -0.746142494  0.107269986  0.216335778  1.868172549 -1.819409350\n [709] -0.816869310  0.797230196  0.387493461  0.116055552 -0.642143367 -1.279534921\n [715] -0.315254451  0.253560552  0.540545029 -0.032779597  0.655660843 -1.068584816\n [721] -0.764498818  1.313707879  0.401545843  1.222140225  0.127270253 -1.023808177\n [727] -1.562044001  1.165714715  0.140294629  1.055904726  0.564384213  0.492273036\n [733] -0.775109612 -0.173425810 -0.824877657  0.676906268 -0.333519870  0.986986421\n [739]  1.428761698  0.993533684  0.232529216 -0.397318052  0.301345667 -0.846576618\n [745]  0.865149507 -0.644587199  0.456379184 -0.389080614 -1.224738689 -0.365518410\n [751] -0.619178394  1.030021367 -0.141380893  1.126645630 -0.426370098 -0.069528169\n [757] -0.278818291 -1.064839314 -1.059068922 -1.499668756  0.288089042 -0.550107312\n [763]  0.488140185  1.138514318 -0.116719778 -0.101078429  1.307419342 -0.276381845\n [769] -0.758885779 -0.711764505  1.511783748  1.639848662  0.244630013  0.221504219\n [775] -0.654177441  0.565151099 -1.892701249  1.807581066 -0.138592586 -0.384863151\n [781] -1.328224445 -0.545025590  0.313656346 -0.742850997  0.555395690  1.764910715\n [787] -0.625257496  0.966956857  0.483320912  0.047494241  0.923103385 -1.145904961\n [793]  0.280204578 -0.157246698  0.225915308  1.372613433  0.453422888  1.838309733\n [799]  1.787117206 -1.365405640 -0.147755898  1.699103944  0.337587490 -1.611704770\n [805]  0.693109756 -0.123305507 -1.155458265  1.664175618 -0.069740496  0.740024799\n [811] -0.293787881 -1.919715622  0.969118202  0.509963921 -0.063905834 -1.903904615\n [817]  0.435070922 -0.292356360  0.653848260 -1.850615935  1.136108383  0.499201243\n [823] -0.327837152 -0.084480052  0.612819677 -0.042708866  1.130712138 -0.239522824\n [829] -0.158551167 -0.315754423 -0.436354058 -1.198617131  0.404851017 -0.909543566\n [835]  0.353229688 -1.744579803 -0.306018192  0.348498095 -0.152707999 -0.716944496\n [841] -1.388874721  1.560990769 -0.095390740 -0.057209488 -0.252022830  1.859187819\n [847] -0.986649113 -0.229759556 -0.731103239  0.718576904 -0.616917852  0.830422764\n [853]  0.157848180  0.110811531 -0.775401222 -1.049415931  0.767900198  1.592467092\n [859] -1.197716793  0.316866704 -0.637006175  0.371941004  0.242307136 -0.605733062\n [865] -0.636043497  0.211652792  1.317679661  1.294389518 -1.626174875  0.800306956\n [871] -1.645112525  0.374144024 -1.200278539  0.314757542 -0.185958625  0.179286536\n [877]  0.252513167  1.689709834 -1.332819517  0.544371027  0.922871979 -0.738319225\n [883] -2.093646010  0.588330996  1.502005197  0.381838790 -0.749886334  1.003194724\n [889]  0.162243597 -1.281816718 -1.014910074  0.947874399 -0.371075597 -0.491180663\n [895]  1.590762608  1.415442514  0.815223669  1.012712401 -0.813600183 -0.247537835\n [901] -0.169390681  0.141832982 -0.552196642  0.271236362 -0.089839801  1.647228922\n [907]  0.766696029 -0.230373260 -0.106136516 -0.400954538 -0.963893353  0.883750528\n [913]  0.523847897  1.819676877  1.412716175 -1.530344689 -1.421245976  0.227205104\n [919] -0.154543403  2.272050984  1.995647085  0.571895742  0.276329023  0.486612398\n [925] -1.228676897 -1.195814899 -0.296225468 -0.254510825  0.321618835  0.108390809\n [931]  0.424887273 -0.265174589 -1.108739132  1.355058261 -0.153658145  0.241236216\n [937]  1.101775429 -0.164691077  1.508008062  0.865835137  1.209149831  1.092928824\n [943]  1.559715532  0.083439438 -1.567778826 -0.865679833 -1.334725613  0.416579762\n [949]  0.707039404 -0.524122618  0.213682421 -0.016493849 -0.696083746 -0.822524183\n [955] -0.178332394 -1.366533614 -0.515273430 -0.753196884  0.280543968 -0.152612933\n [961]  0.388555929  0.802710215  1.231417192 -0.143886010 -0.514767569  3.179693805\n [967] -0.293815620  0.331125630 -0.903287394  0.761178041  0.217058560  0.480523941\n [973] -0.894898261 -0.944038452  0.027258137  2.095712977  0.929867513 -1.682898374\n [979]  2.206324617 -1.597424804 -1.603449672  0.344756802 -1.011927990  2.134007390\n [985] -0.962194757  0.848933882 -1.798772644  0.863766191  0.567741713 -0.231915472\n [991]  0.220258504 -0.140803669  0.591665065 -1.260877269 -2.204095163 -0.003977565\n [997]  0.949783931  1.263807521  1.154929253  0.624069695\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n\n$rawpval\n   [1] 1.246279e-02 3.391133e-01 7.639213e-01 6.261828e-02 4.678558e-01 7.643729e-01\n   [7] 2.344547e-01 4.531955e-01 3.863186e-01 1.098886e-02 1.955115e-01 1.825293e-02\n  [13] 9.208969e-01 5.444218e-01 3.596257e-01 5.121069e-01 8.683836e-01 6.937511e-01\n  [19] 6.689835e-01 3.168993e-03 5.601413e-01 8.894970e-01 5.355354e-01 8.812927e-01\n  [25] 1.314629e-01 5.374392e-01 5.437235e-01 3.566872e-01 3.709885e-01 6.455183e-01\n  [31] 3.941128e-01 9.985277e-02 4.946884e-02 6.023479e-01 1.081900e-01 6.325179e-01\n  [37] 4.687798e-01 8.945297e-02 3.209471e-01 3.546524e-01 1.057214e-01 1.716944e-01\n  [43] 7.224923e-01 8.447545e-02 9.669397e-01 3.448963e-01 1.264460e-01 7.638924e-01\n  [49] 8.297457e-01 2.168661e-01 5.238392e-01 9.086731e-01 8.607500e-01 9.788563e-01\n  [55] 1.175622e-01 6.006550e-01 1.038692e-01 4.523747e-01 4.872292e-01 2.014983e-01\n  [61] 6.207023e-01 5.104944e-01 3.601906e-01 1.003152e-01 7.425621e-01 2.683169e-01\n  [67] 4.182383e-01 8.371515e-02 4.685027e-02 1.994648e-02 9.755920e-01 1.138531e-01\n  [73] 2.160023e-01 3.978748e-01 1.189854e-02 8.537410e-02 5.389309e-01 3.066989e-01\n  [79] 8.785635e-01 9.848065e-01 3.139687e-01 7.519762e-01 2.450273e-02 2.510979e-01\n  [85] 9.307452e-01 4.812028e-01 7.300394e-01 5.907701e-01 4.507695e-01 3.334225e-01\n  [91] 1.045819e-01 8.351643e-02 1.513274e-01 6.919518e-02 1.449571e-01 8.351754e-01\n  [97] 8.176661e-01 7.780199e-01 2.403201e-01 5.901525e-02 4.651873e-01 1.824372e-01\n [103] 9.723536e-01 5.644195e-01 7.183676e-02 6.474877e-02 5.553214e-01 7.389123e-01\n [109] 5.045462e-01 8.695007e-01 8.607456e-02 9.967657e-01 6.898211e-01 6.248980e-01\n [115] 3.615654e-01 2.261779e-01 5.323750e-01 3.265842e-01 3.220248e-01 4.148655e-01\n [121] 9.190462e-01 8.073747e-01 1.053479e-01 6.177177e-01 4.314161e-01 1.576053e-01\n [127] 1.427281e-01 8.188460e-01 6.697835e-01 7.672094e-01 3.313026e-01 7.617987e-01\n [133] 3.280920e-01 8.079481e-01 1.947889e-01 2.787616e-01 4.410817e-01 1.152916e-01\n [139] 4.678895e-01 4.325979e-01 3.907486e-01 8.934136e-01 5.103690e-01 2.007233e-01\n [145] 3.374265e-01 3.428239e-01 7.326295e-01 4.793750e-01 2.535552e-02 4.702522e-01\n [151] 8.916891e-01 2.337318e-01 2.146674e-01 4.866382e-01 4.212340e-01 8.517290e-02\n [157] 5.050141e-01 3.675737e-01 7.993688e-01 3.019884e-02 9.265352e-02 5.270692e-02\n [163] 8.357951e-01 7.138433e-01 1.735347e-02 9.210026e-02 9.156359e-01 9.289307e-01\n [169] 7.812185e-02 1.103277e-01 1.825161e-01 3.016836e-02 7.174509e-01 2.995417e-01\n [175] 8.936002e-01 8.555126e-01 4.006166e-01 7.385744e-02 2.162030e-01 1.386844e-01\n [181] 8.078360e-01 8.351108e-02 2.098925e-01 7.011535e-02 8.602531e-01 2.191214e-01\n [187] 8.989506e-01 6.126512e-01 8.216829e-01 7.923352e-01 2.452899e-01 3.543621e-01\n [193] 1.940643e-01 2.058092e-02 5.249902e-01 4.469011e-01 6.307702e-01 3.423385e-01\n [199] 1.602368e-01 2.452345e-01 5.068118e-01 9.765169e-01 2.370504e-01 4.206951e-01\n [205] 4.811052e-01 6.797799e-01 1.060555e-01 2.565325e-01 7.476947e-01 1.194993e-01\n [211] 3.384431e-01 8.197871e-02 7.744054e-01 3.348553e-01 4.619231e-01 3.431621e-01\n [217] 5.257607e-01 7.811108e-01 9.205415e-01 7.715968e-01 4.783068e-01 1.234646e-01\n [223] 4.745767e-01 9.604770e-01 9.079526e-01 6.474842e-02 8.951715e-01 1.098848e-01\n [229] 3.853417e-01 4.146609e-01 4.052424e-01 4.552361e-01 3.092177e-01 1.378542e-01\n [235] 9.481433e-01 2.048120e-01 4.376742e-01 4.947528e-01 5.040137e-01 6.826370e-01\n [241] 9.384083e-01 2.519123e-02 2.170641e-02 3.862765e-01 3.127676e-01 2.599093e-01\n [247] 5.469864e-01 3.683592e-01 3.408359e-02 3.796982e-01 4.908560e-01 6.438178e-01\n [253] 8.889068e-01 6.778021e-01 4.063745e-02 4.529227e-01 6.185046e-01 3.155783e-01\n [259] 7.948354e-01 7.984707e-01 2.227779e-01 7.316786e-01 7.226398e-01 7.191522e-01\n [265] 3.787902e-01 7.469013e-01 6.790670e-01 6.091077e-01 9.532127e-02 6.945927e-04\n [271] 2.561622e-01 8.055754e-01 8.823465e-01 6.878989e-01 1.344551e-01 9.883361e-01\n [277] 2.262221e-01 2.153685e-01 2.960323e-01 9.896331e-02 3.150660e-01 9.160509e-01\n [283] 8.527111e-01 2.897010e-01 4.666240e-01 1.721149e-01 4.684244e-01 9.771506e-01\n [289] 9.097513e-01 3.356628e-01 4.523799e-01 4.964719e-01 4.174541e-01 1.681142e-01\n [295] 7.446454e-01 9.359240e-01 2.756812e-01 6.535039e-01 1.815396e-01 5.195167e-01\n [301] 7.970382e-01 3.821532e-01 7.455948e-01 4.042808e-01 6.538383e-01 8.114602e-02\n [307] 2.166257e-01 4.336532e-01 6.762572e-01 7.564407e-01 7.377326e-01 4.498853e-01\n [313] 1.697442e-02 5.456900e-02 3.967389e-01 2.037823e-01 1.686492e-01 9.112502e-01\n [319] 6.380108e-01 9.638405e-01 7.457336e-01 3.310672e-01 1.010347e-01 6.287594e-01\n [325] 7.053542e-02 3.674352e-01 7.181936e-01 6.819490e-01 7.078292e-01 9.596563e-01\n [331] 4.568936e-01 1.077132e-01 7.030721e-02 3.975400e-01 1.562794e-01 6.617381e-02\n [337] 6.448586e-01 4.578329e-01 8.103799e-01 4.502196e-01 1.579998e-01 7.577399e-01\n [343] 7.999519e-01 1.561762e-01 2.269475e-01 6.433754e-04 2.446649e-01 8.657837e-01\n [349] 1.493099e-01 7.807120e-01 3.496377e-01 3.313390e-01 5.721846e-01 3.909391e-01\n [355] 7.369813e-01 5.456156e-01 8.380707e-01 8.195769e-01 9.726667e-01 5.458218e-01\n [361] 6.662981e-02 7.672356e-01 3.208229e-01 1.312464e-02 9.065302e-01 9.607558e-01\n [367] 5.459691e-01 1.246283e-01 5.833618e-01 1.924279e-01 4.894028e-01 3.960899e-02\n [373] 7.790156e-02 7.378677e-01 6.421083e-02 7.483306e-02 1.114570e-01 2.441235e-01\n [379] 2.917533e-02 7.131380e-01 3.259144e-02 3.380850e-01 4.830261e-01 9.680198e-01\n [385] 2.237511e-02 2.087452e-01 6.909008e-01 2.042233e-01 7.388613e-01 9.741333e-01\n [391] 1.018786e-01 7.186270e-01 1.244612e-03 2.024117e-01 2.143841e-01 9.042557e-01\n [397] 6.536380e-01 7.590974e-01 4.842328e-01 1.217804e-01 7.662522e-01 8.889909e-01\n [403] 6.590682e-01 8.849978e-01 9.644726e-01 8.973753e-01 4.478071e-01 8.082504e-01\n [409] 6.104864e-01 2.597494e-01 7.529058e-01 9.665386e-01 1.114284e-01 6.138886e-01\n [415] 1.818387e-01 6.424364e-01 3.463021e-01 2.409176e-01 8.099108e-01 1.114402e-01\n [421] 3.830671e-01 3.488475e-01 5.744613e-01 4.741035e-01 1.599661e-01 8.738756e-04\n [427] 2.951641e-01 3.798791e-01 3.846580e-02 6.701189e-01 6.308371e-01 6.833675e-01\n [433] 2.901339e-01 1.551090e-01 2.867172e-01 8.684183e-01 7.077803e-01 1.232164e-01\n [439] 7.668372e-02 2.124649e-01 9.322893e-01 8.879287e-01 9.796764e-01 6.112825e-02\n [445] 7.302736e-01 7.319732e-01 1.615952e-01 1.056132e-01 6.383483e-01 2.189434e-01\n [451] 6.504434e-01 3.426192e-01 5.453444e-01 4.228941e-01 5.581279e-01 6.996139e-01\n [457] 3.621763e-01 8.682967e-02 8.973719e-02 6.263437e-01 1.268998e-01 2.793761e-01\n [463] 6.887198e-01 2.069769e-01 2.981673e-01 8.984941e-01 6.689006e-01 1.934649e-02\n [469] 3.456076e-01 4.451253e-01 5.947612e-01 1.385609e-01 9.177355e-01 7.136189e-01\n [475] 5.233352e-01 2.733670e-01 8.612931e-01 3.464088e-01 3.490432e-01 3.284864e-01\n [481] 2.312437e-01 5.082967e-01 8.312892e-02 6.076036e-01 3.704743e-01 1.558864e-02\n [487] 6.267360e-01 2.072939e-01 9.382966e-01 2.896143e-01 1.586536e-01 4.122598e-01\n [493] 7.595195e-01 3.950704e-01 5.614449e-01 3.324165e-01 1.822235e-01 4.931517e-01\n [499] 8.954508e-01 7.579791e-01 6.963965e-01 4.913298e-01 5.874067e-01 1.858350e-01\n [505] 6.251926e-01 2.593470e-01 4.067619e-01 7.095040e-01 3.829784e-01 4.160574e-01\n [511] 2.231238e-02 1.411135e-01 3.862483e-01 3.098175e-01 2.315901e-01 8.508298e-01\n [517] 4.225827e-02 9.414318e-01 8.007753e-01 9.603588e-01 7.955735e-01 8.979139e-01\n [523] 7.346417e-01 3.838468e-01 8.020765e-01 7.358903e-01 5.071735e-01 6.551688e-01\n [529] 5.800530e-01 6.029481e-01 7.749880e-01 8.587291e-01 2.724027e-01 8.591445e-02\n [535] 4.849269e-01 6.833029e-01 1.795881e-02 5.568616e-01 3.091156e-01 1.491698e-01\n [541] 2.634920e-01 8.497030e-01 7.416515e-01 1.110066e-01 4.507113e-01 1.315685e-01\n [547] 8.733846e-01 8.528573e-01 2.307162e-01 5.473158e-01 3.409930e-01 3.730137e-01\n [553] 3.866203e-02 3.541804e-01 9.675664e-01 2.048668e-01 7.805281e-01 4.633334e-01\n [559] 8.929711e-01 6.501699e-02 6.845552e-02 1.129492e-01 6.936470e-01 2.896556e-01\n [565] 8.482785e-01 9.934963e-01 4.837232e-01 9.652014e-01 5.861628e-01 1.746482e-02\n [571] 5.556886e-01 5.631643e-01 6.678840e-02 2.409644e-01 1.680851e-01 5.884256e-01\n [577] 9.870314e-01 4.956130e-01 4.820595e-01 5.794705e-01 2.508317e-01 4.402338e-01\n [583] 1.237284e-05 3.424451e-01 6.278008e-01 6.184204e-01 9.418100e-01 7.678816e-01\n [589] 3.269605e-01 3.862926e-01 4.321520e-01 7.083556e-01 7.164292e-01 1.493282e-01\n [595] 3.905538e-01 6.620623e-01 9.099314e-01 4.965288e-01 3.541844e-01 8.047785e-01\n [601] 1.056263e-01 3.214171e-01 9.459646e-02 9.479135e-01 8.332198e-01 3.621932e-01\n [607] 8.471269e-01 5.829020e-05 2.882243e-01 6.885192e-01 7.894182e-01 9.608113e-01\n [613] 1.390632e-01 4.137804e-01 1.132903e-01 9.332350e-01 8.422440e-01 1.135942e-01\n [619] 2.508686e-01 1.998119e-01 3.896331e-01 8.897166e-01 7.412723e-01 6.076339e-01\n [625] 2.688946e-02 2.577544e-01 3.141900e-01 1.854800e-01 1.204595e-01 4.383513e-01\n [631] 9.603965e-01 4.006146e-01 3.553479e-01 6.005380e-01 1.427378e-01 1.601173e-01\n [637] 9.216962e-01 3.135525e-01 4.259694e-01 7.172462e-01 7.391820e-01 9.785578e-01\n [643] 8.801332e-01 2.015551e-01 7.635431e-01 5.647864e-01 4.565965e-01 7.353718e-01\n [649] 8.735518e-01 4.495411e-01 4.395403e-01 9.896899e-01 3.901396e-01 1.541610e-02\n [655] 6.259922e-02 4.435807e-01 7.244770e-01 6.448837e-01 8.342102e-01 6.270336e-01\n [661] 9.559119e-01 2.813697e-01 6.371301e-02 1.995325e-01 2.281530e-01 8.953656e-01\n [667] 8.652667e-01 7.880682e-01 4.157442e-01 9.458625e-01 4.708858e-01 1.134207e-01\n [673] 7.857254e-01 1.965119e-02 6.949026e-01 1.759089e-01 8.038168e-01 7.861210e-01\n [679] 6.754208e-01 7.581026e-01 9.500660e-01 8.753272e-01 8.166066e-01 5.615806e-01\n [685] 7.252746e-01 2.734466e-01 7.350211e-01 8.819695e-01 9.567863e-01 8.620825e-01\n [691] 9.270653e-01 9.496268e-01 4.007208e-01 9.641884e-01 4.884434e-01 7.866077e-01\n [697] 5.841185e-01 1.809771e-01 9.392565e-01 3.922322e-01 4.967654e-02 3.385179e-01\n [703] 2.616546e-01 7.722093e-01 4.572874e-01 4.143630e-01 3.086901e-02 9.655755e-01\n [709] 7.929984e-01 2.126587e-01 3.491955e-01 4.538043e-01 7.396099e-01 8.996456e-01\n [715] 6.237158e-01 3.999175e-01 2.944106e-01 5.130748e-01 2.560212e-01 8.573716e-01\n [721] 7.777150e-01 9.447226e-02 3.440091e-01 1.108273e-01 4.493633e-01 8.470371e-01\n [727] 9.408612e-01 1.218649e-01 4.442136e-01 1.455059e-01 2.862463e-01 3.112632e-01\n [733] 7.808626e-01 5.688416e-01 7.952795e-01 2.492327e-01 6.306291e-01 1.618246e-01\n [739] 7.653637e-02 1.602250e-01 4.080635e-01 6.544335e-01 3.815755e-01 8.013844e-01\n [745] 1.934784e-01 7.404026e-01 3.240587e-01 6.513917e-01 8.896632e-01 6.426378e-01\n [751] 7.321006e-01 1.515000e-01 5.562155e-01 1.299462e-01 6.650809e-01 5.277154e-01\n [757] 6.098079e-01 8.565257e-01 8.552158e-01 9.331499e-01 3.866393e-01 7.088771e-01\n [763] 3.127253e-01 1.274529e-01 5.464589e-01 5.402559e-01 9.553517e-02 6.088726e-01\n [769] 7.760396e-01 7.616947e-01 6.529444e-02 5.051832e-02 4.033715e-01 4.123499e-01\n [775] 7.435013e-01 2.859855e-01 9.708012e-01 3.533586e-02 5.551139e-01 6.498306e-01\n [781] 9.079480e-01 7.071320e-01 3.768910e-01 7.712141e-01 2.893120e-01 3.878939e-02\n [787] 7.340990e-01 1.667828e-01 3.144339e-01 4.810597e-01 1.779767e-01 8.740828e-01\n [793] 3.896603e-01 5.624748e-01 4.106336e-01 8.493628e-02 3.251221e-01 3.300839e-02\n [799] 3.695927e-02 9.139372e-01 5.587323e-01 4.464980e-02 3.678370e-01 9.464869e-01\n [805] 2.441203e-01 5.490674e-01 8.760486e-01 4.803867e-02 5.277999e-01 2.296425e-01\n [811] 6.155400e-01 9.725531e-01 1.662431e-01 3.050384e-01 5.254774e-01 9.715387e-01\n [817] 3.317555e-01 6.149929e-01 2.566048e-01 9.678876e-01 1.279556e-01 3.088188e-01\n [823] 6.284826e-01 5.336626e-01 2.699978e-01 5.170332e-01 1.290881e-01 5.946499e-01\n [829] 5.629887e-01 6.239055e-01 6.687101e-01 8.846616e-01 3.427935e-01 8.184684e-01\n [835] 3.619581e-01 9.594710e-01 6.202046e-01 3.637331e-01 5.606857e-01 7.632958e-01\n [841] 9.175646e-01 5.926296e-02 5.379978e-01 5.228108e-01 5.994883e-01 3.150026e-02\n [847] 8.380927e-01 5.908607e-01 7.676420e-01 2.362008e-01 7.313555e-01 2.031499e-01\n [853] 4.372882e-01 4.558829e-01 7.809487e-01 8.530066e-01 2.212732e-01 5.563990e-02\n [859] 8.844864e-01 3.756724e-01 7.379396e-01 3.549684e-01 4.042711e-01 7.276540e-01\n [865] 7.376260e-01 4.161890e-01 9.380545e-02 9.776545e-02 9.480438e-01 2.117665e-01\n [871] 9.500267e-01 3.541486e-01 8.849844e-01 3.764729e-01 5.737614e-01 4.288564e-01\n [877] 4.003222e-01 4.554174e-02 9.087045e-01 2.930931e-01 1.780370e-01 7.698398e-01\n [883] 9.818542e-01 2.781551e-01 6.654788e-02 3.512905e-01 7.733384e-01 1.578835e-01\n [889] 4.355570e-01 9.000465e-01 8.449257e-01 1.715967e-01 6.447094e-01 6.883507e-01\n [895] 5.583151e-02 7.846940e-02 2.074721e-01 1.555988e-01 7.920630e-01 5.977540e-01\n [901] 5.672553e-01 4.436060e-01 7.095932e-01 3.931046e-01 5.357927e-01 4.975550e-02\n [907] 2.216311e-01 5.910991e-01 5.422630e-01 6.557732e-01 8.324503e-01 1.884154e-01\n [913] 3.001922e-01 3.440411e-02 7.886960e-02 9.370343e-01 9.223774e-01 4.101321e-01\n [919] 5.614094e-01 1.154172e-02 2.298618e-02 2.836963e-01 3.911477e-01 3.132665e-01\n [925] 8.904035e-01 8.841156e-01 6.164710e-01 6.004495e-01 3.738707e-01 4.568428e-01\n [931] 3.354594e-01 6.045625e-01 8.662286e-01 8.769950e-02 5.610604e-01 4.046860e-01\n [937] 1.352797e-01 5.654064e-01 6.577623e-02 1.932903e-01 1.133026e-01 1.372125e-01\n [943] 5.941356e-02 4.667511e-01 9.415336e-01 8.066671e-01 9.090169e-01 3.384929e-01\n [949] 2.397710e-01 6.999034e-01 4.153974e-01 5.065798e-01 7.568118e-01 7.946107e-01\n [955] 5.707690e-01 9.141142e-01 6.968190e-01 7.743342e-01 3.895301e-01 5.606482e-01\n [961] 3.488023e-01 2.110711e-01 1.090834e-01 5.572048e-01 6.966423e-01 7.371537e-04\n [967] 6.155506e-01 3.702748e-01 8.168133e-01 2.232754e-01 4.140814e-01 3.154274e-01\n [973] 8.145793e-01 8.274250e-01 4.891269e-01 1.805383e-02 1.762198e-01 9.538026e-01\n [979] 1.368064e-02 9.449145e-01 9.455823e-01 3.651386e-01 8.442138e-01 1.642109e-02\n [985] 8.320241e-01 1.979590e-01 9.639727e-01 1.938582e-01 2.851052e-01 5.916982e-01\n [991] 4.128349e-01 5.559875e-01 2.770374e-01 8.963235e-01 9.862412e-01 5.015868e-01\n [997] 1.711110e-01 1.031496e-01 1.240597e-01 2.662909e-01\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n\n$adjpval\n   [1] 0.52440419 0.89279215 0.96918126 0.71500137 0.92683058 0.96918126 0.85552134 0.92390705\n   [9] 0.90764482 0.50957206 0.84459318 0.55550242 0.99033160 0.94191862 0.90046873 0.93428026\n  [17] 0.98365938 0.96136134 0.95829409 0.32318679 0.94450938 0.98706540 0.93908978 0.98620664\n  [25] 0.78999636 0.93955873 0.94191862 0.90040511 0.90492793 0.95524669 0.90877004 0.76201255\n  [33] 0.68364643 0.94989500 0.76678599 0.95105638 0.92713456 0.74522893 0.88825145 0.90037895\n  [41] 0.76332487 0.82215393 0.96659618 0.73983348 0.99574836 0.89433057 0.78849278 0.96918126\n  [49] 0.97945173 0.84954658 0.93436330 0.98911652 0.98297565 0.99600225 0.77723210 0.94989500\n  [57] 0.76332487 0.92369922 0.92920930 0.84467934 0.94989500 0.93428026 0.90070653 0.76246919\n  [65] 0.96659618 0.87164469 0.91548181 0.73983348 0.67890785 0.56012964 0.99600225 0.77102540\n  [73] 0.84954658 0.90972921 0.52195744 0.73983348 0.94007266 0.88404605 0.98553143 0.99670795\n  [81] 0.88625333 0.96778464 0.57598811 0.85785024 0.99248735 0.92920930 0.96659618 0.94989500\n  [89] 0.92364987 0.89162706 0.76332487 0.73983348 0.80526947 0.72491290 0.80171548 0.98026460\n  [97] 0.97751568 0.97021732 0.85552134 0.70474043 0.92683058 0.83396455 0.99600225 0.94485384\n [105] 0.72840276 0.71821129 0.94326995 0.96659618 0.93269455 0.98396566 0.73983348 0.99931388\n [113] 0.96136134 0.94997549 0.90084961 0.85307736 0.93772744 0.88965472 0.88825145 0.91419625\n [121] 0.99033160 0.97597710 0.76332487 0.94989500 0.91871704 0.80797901 0.80108338 0.97777321\n [129] 0.95843040 0.96960851 0.89149144 0.96918126 0.89078115 0.97597710 0.84430488 0.87677579\n [137] 0.92120385 0.77252268 0.92683058 0.91871704 0.90841074 0.98725551 0.93428026 0.84467934\n [145] 0.89279215 0.89286469 0.96659618 0.92920930 0.58090325 0.92717524 0.98725551 0.85552134\n [153] 0.84954658 0.92920930 0.91710102 0.73983348 0.93269455 0.90356135 0.97455342 0.61141993\n [161] 0.74573792 0.69222715 0.98026460 0.96466729 0.55550242 0.74573792 0.99033160 0.99217648\n [169] 0.73826078 0.76724823 0.83396455 0.61141993 0.96579612 0.88191763 0.98725551 0.98239800\n [177] 0.91035691 0.73498408 0.84954658 0.79534583 0.97597710 0.73983348 0.84776838 0.72808184\n [185] 0.98297565 0.84954658 0.98836922 0.94989500 0.97815524 0.97225660 0.85662176 0.90037895\n [193] 0.84430488 0.56027293 0.93436330 0.92364987 0.95105638 0.89286469 0.80970596 0.85662176\n [201] 0.93376462 0.99600225 0.85552134 0.91710102 0.92920930 0.96094409 0.76332487 0.86381823\n [209] 0.96709810 0.77906166 0.89279215 0.73983348 0.96990995 0.89241398 0.92550037 0.89294385\n [217] 0.93436330 0.97022562 0.99033160 0.96960851 0.92881111 0.78121570 0.92734270 0.99574428\n [225] 0.98911652 0.71821129 0.98735849 0.76724823 0.90764482 0.91419625 0.91328750 0.92488656\n [233] 0.88518253 0.79534583 0.99550697 0.84690914 0.92006215 0.93139884 0.93269455 0.96136134\n [241] 0.99438435 0.58009205 0.56327473 0.90764482 0.88625333 0.86628174 0.94201918 0.90421186\n [249] 0.61825042 0.90750441 0.92981755 0.95524669 0.98706540 0.96074204 0.65128896 0.92390705\n [257] 0.94989500 0.88625333 0.97319768 0.97455342 0.84954658 0.96659618 0.96659618 0.96603648\n [265] 0.90702881 0.96690825 0.96094409 0.94989500 0.74687631 0.16053347 0.86381823 0.97569808\n [273] 0.98628701 0.96136134 0.79145833 0.99730625 0.85307736 0.84954658 0.87983656 0.75937422\n [281] 0.88625333 0.99033160 0.98239800 0.87802341 0.92683058 0.82366058 0.92713456 0.99600225\n [289] 0.98938039 0.89241398 0.92369922 0.93198595 0.91505811 0.81789021 0.96659618 0.99406023\n [297] 0.87677579 0.95570099 0.83382632 0.93436330 0.97382890 0.90764482 0.96659618 0.91283290\n [305] 0.95570099 0.73983348 0.84954658 0.91932781 0.96053842 0.96918126 0.96659618 0.92364987\n [313] 0.55143345 0.69365196 0.90972921 0.84525754 0.81861836 0.98975865 0.95338764 0.99574428\n [321] 0.96659618 0.89139325 0.76321608 0.95072121 0.72840276 0.90356135 0.96603648 0.96136134\n [329] 0.96427395 0.99574428 0.92511571 0.76575832 0.72831489 0.90972921 0.80779157 0.72034206\n [337] 0.95524669 0.92511571 0.97597710 0.92364987 0.80797901 0.96918126 0.97455342 0.80779157\n [345] 0.85307736 0.15585175 0.85660096 0.98330502 0.80446373 0.97022562 0.89739928 0.89149144\n [353] 0.94665099 0.90841074 0.96659618 0.94201918 0.98040526 0.97790238 0.99600225 0.94201918\n [361] 0.72034206 0.96960851 0.88825145 0.53565961 0.98911652 0.99574428 0.94201918 0.78444662\n [369] 0.94926410 0.84381453 0.92938872 0.64366820 0.73826078 0.96659618 0.71719276 0.73826078\n [377] 0.76724823 0.85647951 0.61083471 0.96466729 0.61801385 0.89279215 0.92920930 0.99574836\n [385] 0.56835783 0.84776838 0.96136134 0.84635875 0.96659618 0.99600225 0.76321608 0.96603648\n [393] 0.20579187 0.84467934 0.84938121 0.98866978 0.95570099 0.96918126 0.92920930 0.78020587\n [401] 0.96933341 0.98706540 0.95654615 0.98655236 0.99574428 0.98808580 0.92364987 0.97597710\n [409] 0.94989500 0.86628174 0.96802170 0.99574428 0.76724823 0.94989500 0.83382632 0.95524669\n [417] 0.89504087 0.85552134 0.97597710 0.76724823 0.90764482 0.89739928 0.94665099 0.92724817\n [425] 0.80970596 0.17339037 0.87936691 0.90750441 0.64321996 0.95843040 0.95105638 0.96136134\n [433] 0.87802341 0.80630401 0.87708773 0.98365938 0.96427395 0.78121570 0.73826078 0.84938121\n [441] 0.99249315 0.98706540 0.99600225 0.71099089 0.96659618 0.96659618 0.80970596 0.76332487\n [449] 0.95338764 0.84954658 0.95531998 0.89286469 0.94191862 0.91714596 0.94350704 0.96212468\n [457] 0.90084961 0.74060640 0.74522893 0.95014213 0.78889384 0.87677579 0.96136134 0.84737522\n [465] 0.88150904 0.98836922 0.95829409 0.56012964 0.89468103 0.92299373 0.94989500 0.79534583\n [473] 0.99033160 0.96466729 0.93436330 0.87665020 0.98297565 0.89504087 0.89739928 0.89078115\n [481] 0.85316301 0.93428026 0.73983348 0.94989500 0.90492793 0.54426323 0.95052684 0.84737522\n [489] 0.99434766 0.87802341 0.80939689 0.91419625 0.96918126 0.90948050 0.94463787 0.89162706\n [497] 0.83396455 0.93090267 0.98748428 0.96918126 0.96136134 0.92998932 0.94960002 0.83684137\n [505] 0.94997549 0.86585499 0.91357642 0.96462062 0.90764482 0.91443288 0.56835783 0.79786953\n [513] 0.90764482 0.88519210 0.85316301 0.98239800 0.66141372 0.99514633 0.97480076 0.99574428\n [521] 0.97319768 0.98812890 0.96659618 0.90764482 0.97480076 0.96659618 0.93383635 0.95584981\n [529] 0.94827850 0.94989500 0.97021732 0.98261282 0.87523104 0.73983348 0.92920930 0.96136134\n [537] 0.55550242 0.94339013 0.88518253 0.80446373 0.86712344 0.98238323 0.96659618 0.76724823\n [545] 0.92364987 0.78999636 0.98472112 0.98239800 0.85316301 0.94201918 0.89286469 0.90492793\n [553] 0.64321996 0.90025929 0.99574836 0.84690914 0.97022562 0.92666296 0.98725551 0.71821129\n [561] 0.72214112 0.76911628 0.96136134 0.87802341 0.98203855 0.99813163 0.92920930 0.99574428\n [569] 0.94960002 0.55550242 0.94326995 0.94472181 0.72034206 0.85552134 0.81789021 0.94980286\n [577] 0.99722621 0.93160571 0.92920930 0.94827850 0.85785024 0.92102828 0.02531949 0.89286469\n [585] 0.95072121 0.94989500 0.99514633 0.96960851 0.88999781 0.90764482 0.91871704 0.96438954\n [593] 0.96531365 0.80446373 0.90841074 0.95745136 0.98949285 0.93198595 0.90025929 0.97569808\n [601] 0.76332487 0.88825145 0.74573792 0.99550697 0.98026460 0.90084961 0.98185659 0.05011791\n [609] 0.87802341 0.96136134 0.97225660 0.99574428 0.79534583 0.91419625 0.77025951 0.99249315\n [617] 0.98124854 0.77037508 0.85785024 0.84467934 0.90841074 0.98706540 0.96659618 0.94989500\n [625] 0.59485595 0.86412672 0.88625333 0.83611852 0.78012432 0.92030222 0.99574428 0.91035691\n [633] 0.90037895 0.94989500 0.80108338 0.80970596 0.99033160 0.88625333 0.91804157 0.96578870\n [641] 0.96659618 0.99600225 0.98553143 0.84467934 0.96918126 0.94485384 0.92511571 0.96659618\n [649] 0.98472112 0.92364987 0.92082994 0.99742369 0.90841074 0.54426323 0.71500137 0.92188710\n [657] 0.96659618 0.95524669 0.98026460 0.95056645 0.99574428 0.87677579 0.71719276 0.84467934\n [665] 0.85307736 0.98747479 0.98330502 0.97209469 0.91422673 0.99535976 0.92724817 0.77025951\n [673] 0.97150277 0.56012964 0.96136134 0.82920205 0.97563506 0.97150277 0.96053842 0.96918126\n [681] 0.99558689 0.98490774 0.97738765 0.94463787 0.96659618 0.87665020 0.96659618 0.98627946\n [689] 0.99574428 0.98297565 0.99206447 0.99550697 0.91035691 0.99574428 0.92926022 0.97164978\n [697] 0.94926410 0.83270127 0.99475257 0.90877004 0.68364643 0.89279215 0.86678945 0.96960851\n [705] 0.92511571 0.91419625 0.61549001 0.99574428 0.97245421 0.84938121 0.89739928 0.92390705\n [713] 0.96659618 0.98836922 0.94989553 0.90997468 0.87934544 0.93428026 0.86381823 0.98261282\n [721] 0.97021732 0.74573792 0.89323253 0.76724823 0.92364987 0.98185659 0.99514633 0.78020587\n [729] 0.92210250 0.80171548 0.87708773 0.88568119 0.97022562 0.94613754 0.97319768 0.85785024\n [737] 0.95105638 0.80970596 0.73826078 0.80970596 0.91383833 0.95570099 0.90764482 0.97480076\n [745] 0.84430488 0.96659618 0.88825145 0.95531998 0.98706540 0.95524669 0.96659618 0.80526947\n [753] 0.94337180 0.78902105 0.95826536 0.93488396 0.94989500 0.98261282 0.98239800 0.99249315\n [761] 0.90764482 0.96462062 0.88625333 0.78889384 0.94201918 0.94083685 0.74719044 0.94989500\n [769] 0.97021732 0.96918126 0.71913102 0.68466885 0.91247498 0.91419625 0.96659618 0.87708773\n [777] 0.99594444 0.62428306 0.94326995 0.95531998 0.98911652 0.96390104 0.90636336 0.96960851\n [785] 0.87802341 0.64321996 0.96659618 0.81686125 0.88625333 0.92920930 0.83073180 0.98478256\n [793] 0.90841074 0.94472181 0.91388186 0.73983348 0.88855692 0.61814328 0.64147782 0.99033160\n [801] 0.94380751 0.67260782 0.90356135 0.99550697 0.85647951 0.94207038 0.98490774 0.68008202\n [809] 0.93488396 0.85316301 0.94989500 0.99600225 0.81615431 0.88290376 0.93436330 0.99600225\n [817] 0.89153087 0.94989500 0.86381823 0.99574836 0.78889384 0.88518253 0.95072121 0.93827387\n [825] 0.87272218 0.93432973 0.78889384 0.94989500 0.94472181 0.94989553 0.95829409 0.98655236\n [833] 0.89286469 0.97777321 0.90084961 0.99574428 0.94989500 0.90195451 0.94450938 0.96918126\n [841] 0.99033160 0.70651734 0.93957585 0.93436330 0.94989500 0.61549001 0.98040526 0.94989500\n [849] 0.96960851 0.85552134 0.96659618 0.84492460 0.92006215 0.92511571 0.97022562 0.98239800\n [857] 0.84954658 0.69804254 0.98655236 0.90521416 0.96659618 0.90037895 0.91283290 0.96659618\n [865] 0.96659618 0.91444679 0.74573792 0.75501860 0.99550697 0.84818397 0.99558689 0.90025929\n [873] 0.98655236 0.90634708 0.94665099 0.91871704 0.91025311 0.67376247 0.98911652 0.87909252\n [881] 0.83073180 0.96960851 0.99618620 0.87677579 0.72034206 0.89867198 0.96990995 0.80797901\n [889] 0.92006215 0.98836922 0.98157879 0.82215393 0.95524669 0.96136134 0.69937073 0.73826078\n [897] 0.84751570 0.80706241 0.97225660 0.94989500 0.94533815 0.92188710 0.96462062 0.90877004\n [905] 0.93908978 0.68364643 0.84954658 0.94989500 0.94191862 0.95627135 0.98019402 0.83975497\n [913] 0.88191763 0.61997274 0.73826078 0.99417822 0.99033160 0.91388186 0.94463787 0.51865333\n [921] 0.57045237 0.87708773 0.90841074 0.88625333 0.98706540 0.98655236 0.94989500 0.94989500\n [929] 0.90492793 0.92511571 0.89241398 0.94989500 0.98330502 0.74222553 0.94463787 0.91322118\n [937] 0.79376785 0.94485384 0.71934764 0.84430488 0.77025951 0.79534583 0.70720158 0.92683058\n [945] 0.99514633 0.97597710 0.98911652 0.89279215 0.85552134 0.96212468 0.91419625 0.93376462\n [953] 0.96918126 0.97304349 0.94665099 0.99033160 0.96136134 0.96990995 0.90841074 0.94450938\n [961] 0.89739928 0.84776838 0.76693423 0.94339013 0.96136134 0.16286312 0.94989500 0.90492793\n [969] 0.97749292 0.85044736 0.91419625 0.88625333 0.97691106 0.97910820 0.92938872 0.55550242\n [977] 0.82975805 0.99574428 0.54426323 0.99514633 0.99528290 0.90250142 0.98157879 0.54426323\n [985] 0.98005434 0.84467934 0.99574428 0.84430488 0.87708773 0.94989500 0.91419625 0.94329711\n [993] 0.87677579 0.98768658 0.99722567 0.93243385 0.82215393 0.76332487 0.78354454 0.86854934\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n"} -->

```
$DEindices
 [1]   583  2874  3217  4459  4773  4862  4863  4864  5020  5457  6129  6601  7119 10197

$TestStatistic
   [1]  2.242554159  0.414884418 -0.718973343  1.533159512  0.080660917 -0.720439972
   [7]  0.724254738  0.117591844  0.288926933  2.290752879  0.857763511  2.091247791
  [13] -1.411130605 -0.111580001  0.359459531 -0.030352195 -1.118782961 -0.506511348
  [19] -0.437108039  2.729763077 -0.151327480 -1.223857364 -0.089192100 -1.181473516
  [25]  1.119502736 -0.093984416 -0.109818817  0.367327953  0.329236294 -0.373248755
  [31]  0.268615437  1.282390929  1.650025705 -0.259429046  1.236211499 -0.338529602
  [37]  0.078337405  1.344131251  0.465052024  0.372790052  1.249608032  0.947490772
  [43] -0.590261472  1.375582568 -1.837605869  0.399136492  1.143353033 -0.718879490
  [49] -0.953160701  0.782820964 -0.059791524 -1.332628428 -1.083695080 -2.030681020
  [55]  1.187261682 -0.255042812  1.259808547  0.119663902  0.032017051  0.836281313
  [61] -0.307325919 -0.026308483  0.357949329  1.279757601 -0.651264592  0.617911207
  [67]  0.206402496  1.380507959  1.676192217  2.054855438 -1.970195349  1.206288629
  [73]  0.785765950  0.258851909  2.260389582  1.369803650 -0.097740741  0.505229152
  [79] -1.167835704 -2.165008980  0.484632009 -0.680721750  1.968544126  0.671038858
  [85] -1.481364084  0.047135166 -0.612932046 -0.229526407  0.123717554  0.430482130
  [91]  1.255867931  1.381800841  1.030757152  1.481811869  1.058309645 -0.974820767
  [97] -0.906506394 -0.765523013  0.705273271  1.563093954  0.087373539  0.906116050
 [103] -1.916567554 -0.162183871  1.462247078  1.516086297 -0.139117743 -0.639995625
 [109] -0.011395814 -1.124034066  1.365330759 -2.723028217 -0.495343435 -0.318370495
 [115]  0.354277671  0.751493378 -0.081241362  0.449364891  0.462044310  0.215046477
 [121] -1.398684381 -0.868262644  1.251654556 -0.299492083  0.172769903  1.004348480
 [127]  1.068142447 -0.910976198 -0.439315479 -0.729687472  0.436319054 -0.712100305
 [133]  0.445187842 -0.870359748  0.860383460  0.586524296  0.148227361  1.198857948
 [139]  0.080576201  0.169763896  0.277368387 -1.244888499 -0.025994148  0.839040325
 [145]  0.419496872  0.404768248 -0.620785046  0.051722230  1.953916924  0.074635929
 [151] -1.235561090  0.726612049  0.790330539  0.033499224  0.198737614  1.371093559
 [157] -0.012568716  0.338286377 -0.839368863  1.877879228  1.324590404  1.619155389
 [163] -0.977322006 -0.564647795  2.111761107  1.327932147 -1.376301828 -1.467873256
 [169]  1.417818739  1.224787469  0.905817873  1.878325009 -0.575285438  0.525719090
 [175] -1.245904008 -1.060373127  0.251751324  1.447650308  0.785081006  1.086249078
 [181] -0.869949441  1.381835691  0.806794270  1.474932481 -1.081457327  0.775163884
 [187] -1.275595055 -0.286235782 -0.921797673 -0.814550631  0.689386861  0.373570188
 [193]  0.863016228  2.041896126 -0.062682084  0.133494570 -0.333894045  0.406089383
 [199]  0.993485044  0.689562935 -0.017075593 -1.986604868  0.715822700  0.200115647
 [205]  0.047379995 -0.467083415  1.247781560  0.654072522 -0.667253053  1.177493720
 [211]  0.416715815  1.391884365 -0.753434019  0.426545115  0.095590013  0.403848422
 [217] -0.064617416 -0.775950098 -1.408723219 -0.744115887  0.054403555  1.157840319
 [223]  0.063769852 -1.756248471 -1.328252052  1.516089016 -1.254509334  1.227140847
 [229]  0.291481007  0.215571464  0.239800632  0.112442969  0.498069051  1.090010699
 [235] -1.627111454  0.824555543  0.156868659  0.013153113 -0.010061081 -0.475085604
 [241] -1.541548361  1.956702416  2.019716373  0.289036982  0.488020629  0.643625081
 [247] -0.118051143  0.336202084  1.823900091  0.306273521  0.022922564 -0.368682362
 [253] -1.220734728 -0.461561585  1.743336227  0.118280419 -0.301555654  0.480099477
 [259] -0.823314357 -0.836171339  0.762845079 -0.617897512 -0.590701476 -0.580324708
 [265]  0.308659710 -0.664770186 -0.465091358 -0.276994121  1.308680682  3.196888672
 [271]  0.655222573 -0.861706383 -1.186798759 -0.489903441  1.105575749 -2.268024231
 [277]  0.751346463  0.787931023  0.535846547  1.287481181  0.481541148 -1.378989117
 [283] -1.048131828  0.554258282  0.083759059  0.945840969  0.079231091 -1.998164251
 [289] -1.339224868  0.424329407  0.119650695  0.008843832  0.208410851  0.961643933
 [295] -0.657733848 -1.521429982  0.595719801 -0.394797648  0.909513539 -0.048940741
 [301] -0.831088413  0.299830501 -0.660691074  0.242282028 -0.395704094  1.397404248
 [307]  0.783640044  0.167080907 -0.457257962 -0.694898927 -0.636370676  0.125951135
 [313]  2.120678852  1.602079617  0.261797117  0.828187135  0.959516846 -1.348494051
 [319] -0.353146901 -1.797105327 -0.661123955  0.436968218  1.275677959 -0.328569434
 [325]  1.471815039  0.338653983 -0.577483755 -0.473155792 -0.547054174 -1.746711063
 [331]  0.108262725  1.238781765  1.473506870  0.259719708  1.009867388  1.504908408
 [337] -0.371476369  0.105894704 -0.879297007  0.125106559  1.002712606 -0.699050803
 [343] -0.841449382  1.010298298  0.748937330  3.218918452  0.691375329 -1.106679230
 [349]  1.039397656 -0.774600009  0.386298651  0.436218733 -0.181938674  0.276872262
 [355] -0.634066592 -0.114591627 -0.986559481 -0.913753876 -1.921515951 -0.115111874
 [361]  1.501371008 -0.729773309  0.465398951  2.222503870 -1.319686546 -1.759525052
 [367] -0.115483549  1.152156934 -0.210501374  0.868983971  0.026566455  1.755241633
 [373]  1.419328978 -0.636785604  1.520355505  1.440711808  1.218815951  0.693099624
 [379]  1.893054276 -0.562575378  1.844001826  0.417695221  0.042560090 -1.852455400
 [385]  2.006994678  0.810782970 -0.498405229  0.826630226 -0.639838798 -1.945345801
 [391]  1.270919535 -0.578767636  3.024648204  0.833037889  0.791301286 -1.306187837
 [397] -0.395161066 -0.703401984  0.039532854  1.166132572 -0.726559776 -1.221178900
 [403] -0.409921356 -1.200347507 -1.805127045 -1.266736741  0.131203679 -0.871467076
 [409] -0.280587004  0.644118066 -0.683662244 -1.832191614  1.218966818 -0.289468637
 [415]  0.908379986 -0.364978718  0.395323367  0.703354105 -0.877567479  1.218904623
 [421]  0.297435398  0.388433939 -0.187743836  0.064958650  0.994597390  3.130052398
 [427]  0.538360576  0.305798228  1.768774012 -0.440241447 -0.334071367 -0.477136414
 [433]  0.552993525  1.014764878  0.563000768 -1.118945449 -0.546911831  1.159057132
 [439]  1.427737431  0.797897839 -1.493060415 -1.215586120 -2.047110235  1.545371203
 [445] -0.613640660 -0.618791660  0.987923113  1.250200474 -0.354047334  0.775766487
 [451] -0.386517844  0.405325433 -0.113907316  0.194495258 -0.146224420 -0.523290361
 [457]  0.352647695  1.360539259  1.342375177 -0.322184946  1.141169127  0.584695938
 [463] -0.492224894  0.816955673  0.529678988 -1.273017436 -0.436879328  2.067437254
 [469]  0.397206551  0.137987183 -0.239810064  1.086807567 -1.389999580 -0.563988074
 [475] -0.058526187  0.602661493 -1.086147311  0.395034356  0.387905027  0.444096594
 [481]  0.734757025 -0.020798153  1.384328680 -0.273078506  0.330597313  2.154802669
 [487] -0.323220823  0.815846901 -1.540630111  0.554511833  1.000006883  0.221735773
 [493] -0.704757645  0.266127770 -0.154633523  0.433250205  0.906923960  0.017166998
 [499] -1.256048345 -0.699816640 -0.514064357  0.021734626 -0.220879113  0.893349660
 [505] -0.319147384  0.645359979  0.235882687 -0.551936223  0.297667702  0.211990074
 [511]  2.008174499  1.075330160  0.289110907  0.496367698  0.733620164 -1.039998982
 [517]  1.725060673 -1.566907040 -0.844393882 -1.754865106 -0.825913735 -1.269754470
 [523] -0.626912371  0.295392987 -0.849061679 -0.630726596 -0.017982387 -0.399313316
 [529] -0.202029049 -0.260985266 -0.755375165 -1.074626883  0.605562418  1.366350762
 [535]  0.037791621 -0.476955060  2.097858848 -0.143016967  0.498358820  1.040000433
 [541]  0.632616566 -1.035160550 -0.648445192  1.221192501  0.123864338  1.119007245
 [547] -1.142537034 -1.048766907  0.736490132 -0.118882667  0.409754676  0.323881887
 [553]  1.766428065  0.374058456 -1.846172618  0.824362510 -0.773977903  0.092039395
 [559] -1.242484436  1.513967871  1.487392989  1.210992026 -0.506214603  0.554391162
 [565] -1.029077897 -2.483568408  0.040811298 -1.814523116 -0.217685244  2.109173047
 [571] -0.140047156 -0.158996856  1.500145216  0.703203615  0.961759847 -0.223496873
 [577] -2.227152100  0.010996819  0.044985272 -0.200539204  0.671874725  0.150376483
 [583]  4.217106879  0.405799241 -0.326034277 -0.301334750 -1.570151233 -0.731888139
 [589]  0.448321748  0.288994905  0.170897902 -0.548587138 -0.572266167  1.039318881
 [595]  0.277876028 -0.418097969 -1.340332692  0.008701103  0.374047857 -0.858814125
 [601]  1.250128745  0.463739874  1.312970406 -1.624951230 -0.966967169  0.352602700
 [607] -1.024188759  3.853207731  0.558579582 -0.491657360 -0.804403989 -1.760179000
 [613]  1.084537797  0.217830935  1.209214321 -1.500325967 -1.003723458  1.207633364
 [619]  0.671759043  0.842293475  0.280275410 -1.225022151 -0.647272923 -0.273157403
 [625]  1.928613007  0.650284012  0.484008192  0.894676720  1.172692876  0.155150631
 [631] -1.755305869  0.251756490  0.370921885 -0.254739995  1.068099504  0.993975775
 [637] -1.416573602  0.485805520  0.186645164 -0.574680194 -0.640825444 -2.024833927
 [643] -1.175653017  0.836079598 -0.717746032 -0.163115914  0.109011874 -0.629141519
 [649] -1.143342464  0.126820938  0.152134858 -2.314868038  0.278955172  2.159231600
 [655]  1.533314260  0.141896834 -0.596193420 -0.371543612 -0.970937171 -0.324006916
 [661] -1.705097223  0.578777279  1.524331113  0.843292120  0.744943234 -1.255578607
 [667] -1.104291729 -0.799736256  0.212793147 -1.605994778  0.073043387  1.208535238
 [673] -0.791676765  2.061006875 -0.509795489  0.931069228 -0.855333825 -0.793034118
 [679] -0.454931609 -0.700212170 -1.645494352 -1.151940540 -0.902508695 -0.154977808
 [685] -0.598583325  0.602421964 -0.628070467 -1.184889977 -1.714551766 -1.089723411
 [691] -1.454277342 -1.641245949  0.251481837 -1.801505541  0.028972061 -0.794705764
 [697] -0.212440944  0.911647707 -1.548562095  0.273505823  1.647997963  0.416511411
 [703]  0.638252679 -0.746142494  0.107269986  0.216335778  1.868172549 -1.819409350
 [709] -0.816869310  0.797230196  0.387493461  0.116055552 -0.642143367 -1.279534921
 [715] -0.315254451  0.253560552  0.540545029 -0.032779597  0.655660843 -1.068584816
 [721] -0.764498818  1.313707879  0.401545843  1.222140225  0.127270253 -1.023808177
 [727] -1.562044001  1.165714715  0.140294629  1.055904726  0.564384213  0.492273036
 [733] -0.775109612 -0.173425810 -0.824877657  0.676906268 -0.333519870  0.986986421
 [739]  1.428761698  0.993533684  0.232529216 -0.397318052  0.301345667 -0.846576618
 [745]  0.865149507 -0.644587199  0.456379184 -0.389080614 -1.224738689 -0.365518410
 [751] -0.619178394  1.030021367 -0.141380893  1.126645630 -0.426370098 -0.069528169
 [757] -0.278818291 -1.064839314 -1.059068922 -1.499668756  0.288089042 -0.550107312
 [763]  0.488140185  1.138514318 -0.116719778 -0.101078429  1.307419342 -0.276381845
 [769] -0.758885779 -0.711764505  1.511783748  1.639848662  0.244630013  0.221504219
 [775] -0.654177441  0.565151099 -1.892701249  1.807581066 -0.138592586 -0.384863151
 [781] -1.328224445 -0.545025590  0.313656346 -0.742850997  0.555395690  1.764910715
 [787] -0.625257496  0.966956857  0.483320912  0.047494241  0.923103385 -1.145904961
 [793]  0.280204578 -0.157246698  0.225915308  1.372613433  0.453422888  1.838309733
 [799]  1.787117206 -1.365405640 -0.147755898  1.699103944  0.337587490 -1.611704770
 [805]  0.693109756 -0.123305507 -1.155458265  1.664175618 -0.069740496  0.740024799
 [811] -0.293787881 -1.919715622  0.969118202  0.509963921 -0.063905834 -1.903904615
 [817]  0.435070922 -0.292356360  0.653848260 -1.850615935  1.136108383  0.499201243
 [823] -0.327837152 -0.084480052  0.612819677 -0.042708866  1.130712138 -0.239522824
 [829] -0.158551167 -0.315754423 -0.436354058 -1.198617131  0.404851017 -0.909543566
 [835]  0.353229688 -1.744579803 -0.306018192  0.348498095 -0.152707999 -0.716944496
 [841] -1.388874721  1.560990769 -0.095390740 -0.057209488 -0.252022830  1.859187819
 [847] -0.986649113 -0.229759556 -0.731103239  0.718576904 -0.616917852  0.830422764
 [853]  0.157848180  0.110811531 -0.775401222 -1.049415931  0.767900198  1.592467092
 [859] -1.197716793  0.316866704 -0.637006175  0.371941004  0.242307136 -0.605733062
 [865] -0.636043497  0.211652792  1.317679661  1.294389518 -1.626174875  0.800306956
 [871] -1.645112525  0.374144024 -1.200278539  0.314757542 -0.185958625  0.179286536
 [877]  0.252513167  1.689709834 -1.332819517  0.544371027  0.922871979 -0.738319225
 [883] -2.093646010  0.588330996  1.502005197  0.381838790 -0.749886334  1.003194724
 [889]  0.162243597 -1.281816718 -1.014910074  0.947874399 -0.371075597 -0.491180663
 [895]  1.590762608  1.415442514  0.815223669  1.012712401 -0.813600183 -0.247537835
 [901] -0.169390681  0.141832982 -0.552196642  0.271236362 -0.089839801  1.647228922
 [907]  0.766696029 -0.230373260 -0.106136516 -0.400954538 -0.963893353  0.883750528
 [913]  0.523847897  1.819676877  1.412716175 -1.530344689 -1.421245976  0.227205104
 [919] -0.154543403  2.272050984  1.995647085  0.571895742  0.276329023  0.486612398
 [925] -1.228676897 -1.195814899 -0.296225468 -0.254510825  0.321618835  0.108390809
 [931]  0.424887273 -0.265174589 -1.108739132  1.355058261 -0.153658145  0.241236216
 [937]  1.101775429 -0.164691077  1.508008062  0.865835137  1.209149831  1.092928824
 [943]  1.559715532  0.083439438 -1.567778826 -0.865679833 -1.334725613  0.416579762
 [949]  0.707039404 -0.524122618  0.213682421 -0.016493849 -0.696083746 -0.822524183
 [955] -0.178332394 -1.366533614 -0.515273430 -0.753196884  0.280543968 -0.152612933
 [961]  0.388555929  0.802710215  1.231417192 -0.143886010 -0.514767569  3.179693805
 [967] -0.293815620  0.331125630 -0.903287394  0.761178041  0.217058560  0.480523941
 [973] -0.894898261 -0.944038452  0.027258137  2.095712977  0.929867513 -1.682898374
 [979]  2.206324617 -1.597424804 -1.603449672  0.344756802 -1.011927990  2.134007390
 [985] -0.962194757  0.848933882 -1.798772644  0.863766191  0.567741713 -0.231915472
 [991]  0.220258504 -0.140803669  0.591665065 -1.260877269 -2.204095163 -0.003977565
 [997]  0.949783931  1.263807521  1.154929253  0.624069695
 [ reached getOption("max.print") -- omitted 11897 entries ]

$rawpval
   [1] 1.246279e-02 3.391133e-01 7.639213e-01 6.261828e-02 4.678558e-01 7.643729e-01
   [7] 2.344547e-01 4.531955e-01 3.863186e-01 1.098886e-02 1.955115e-01 1.825293e-02
  [13] 9.208969e-01 5.444218e-01 3.596257e-01 5.121069e-01 8.683836e-01 6.937511e-01
  [19] 6.689835e-01 3.168993e-03 5.601413e-01 8.894970e-01 5.355354e-01 8.812927e-01
  [25] 1.314629e-01 5.374392e-01 5.437235e-01 3.566872e-01 3.709885e-01 6.455183e-01
  [31] 3.941128e-01 9.985277e-02 4.946884e-02 6.023479e-01 1.081900e-01 6.325179e-01
  [37] 4.687798e-01 8.945297e-02 3.209471e-01 3.546524e-01 1.057214e-01 1.716944e-01
  [43] 7.224923e-01 8.447545e-02 9.669397e-01 3.448963e-01 1.264460e-01 7.638924e-01
  [49] 8.297457e-01 2.168661e-01 5.238392e-01 9.086731e-01 8.607500e-01 9.788563e-01
  [55] 1.175622e-01 6.006550e-01 1.038692e-01 4.523747e-01 4.872292e-01 2.014983e-01
  [61] 6.207023e-01 5.104944e-01 3.601906e-01 1.003152e-01 7.425621e-01 2.683169e-01
  [67] 4.182383e-01 8.371515e-02 4.685027e-02 1.994648e-02 9.755920e-01 1.138531e-01
  [73] 2.160023e-01 3.978748e-01 1.189854e-02 8.537410e-02 5.389309e-01 3.066989e-01
  [79] 8.785635e-01 9.848065e-01 3.139687e-01 7.519762e-01 2.450273e-02 2.510979e-01
  [85] 9.307452e-01 4.812028e-01 7.300394e-01 5.907701e-01 4.507695e-01 3.334225e-01
  [91] 1.045819e-01 8.351643e-02 1.513274e-01 6.919518e-02 1.449571e-01 8.351754e-01
  [97] 8.176661e-01 7.780199e-01 2.403201e-01 5.901525e-02 4.651873e-01 1.824372e-01
 [103] 9.723536e-01 5.644195e-01 7.183676e-02 6.474877e-02 5.553214e-01 7.389123e-01
 [109] 5.045462e-01 8.695007e-01 8.607456e-02 9.967657e-01 6.898211e-01 6.248980e-01
 [115] 3.615654e-01 2.261779e-01 5.323750e-01 3.265842e-01 3.220248e-01 4.148655e-01
 [121] 9.190462e-01 8.073747e-01 1.053479e-01 6.177177e-01 4.314161e-01 1.576053e-01
 [127] 1.427281e-01 8.188460e-01 6.697835e-01 7.672094e-01 3.313026e-01 7.617987e-01
 [133] 3.280920e-01 8.079481e-01 1.947889e-01 2.787616e-01 4.410817e-01 1.152916e-01
 [139] 4.678895e-01 4.325979e-01 3.907486e-01 8.934136e-01 5.103690e-01 2.007233e-01
 [145] 3.374265e-01 3.428239e-01 7.326295e-01 4.793750e-01 2.535552e-02 4.702522e-01
 [151] 8.916891e-01 2.337318e-01 2.146674e-01 4.866382e-01 4.212340e-01 8.517290e-02
 [157] 5.050141e-01 3.675737e-01 7.993688e-01 3.019884e-02 9.265352e-02 5.270692e-02
 [163] 8.357951e-01 7.138433e-01 1.735347e-02 9.210026e-02 9.156359e-01 9.289307e-01
 [169] 7.812185e-02 1.103277e-01 1.825161e-01 3.016836e-02 7.174509e-01 2.995417e-01
 [175] 8.936002e-01 8.555126e-01 4.006166e-01 7.385744e-02 2.162030e-01 1.386844e-01
 [181] 8.078360e-01 8.351108e-02 2.098925e-01 7.011535e-02 8.602531e-01 2.191214e-01
 [187] 8.989506e-01 6.126512e-01 8.216829e-01 7.923352e-01 2.452899e-01 3.543621e-01
 [193] 1.940643e-01 2.058092e-02 5.249902e-01 4.469011e-01 6.307702e-01 3.423385e-01
 [199] 1.602368e-01 2.452345e-01 5.068118e-01 9.765169e-01 2.370504e-01 4.206951e-01
 [205] 4.811052e-01 6.797799e-01 1.060555e-01 2.565325e-01 7.476947e-01 1.194993e-01
 [211] 3.384431e-01 8.197871e-02 7.744054e-01 3.348553e-01 4.619231e-01 3.431621e-01
 [217] 5.257607e-01 7.811108e-01 9.205415e-01 7.715968e-01 4.783068e-01 1.234646e-01
 [223] 4.745767e-01 9.604770e-01 9.079526e-01 6.474842e-02 8.951715e-01 1.098848e-01
 [229] 3.853417e-01 4.146609e-01 4.052424e-01 4.552361e-01 3.092177e-01 1.378542e-01
 [235] 9.481433e-01 2.048120e-01 4.376742e-01 4.947528e-01 5.040137e-01 6.826370e-01
 [241] 9.384083e-01 2.519123e-02 2.170641e-02 3.862765e-01 3.127676e-01 2.599093e-01
 [247] 5.469864e-01 3.683592e-01 3.408359e-02 3.796982e-01 4.908560e-01 6.438178e-01
 [253] 8.889068e-01 6.778021e-01 4.063745e-02 4.529227e-01 6.185046e-01 3.155783e-01
 [259] 7.948354e-01 7.984707e-01 2.227779e-01 7.316786e-01 7.226398e-01 7.191522e-01
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 [331] 4.568936e-01 1.077132e-01 7.030721e-02 3.975400e-01 1.562794e-01 6.617381e-02
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 [343] 7.999519e-01 1.561762e-01 2.269475e-01 6.433754e-04 2.446649e-01 8.657837e-01
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 [361] 6.662981e-02 7.672356e-01 3.208229e-01 1.312464e-02 9.065302e-01 9.607558e-01
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 [391] 1.018786e-01 7.186270e-01 1.244612e-03 2.024117e-01 2.143841e-01 9.042557e-01
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 [415] 1.818387e-01 6.424364e-01 3.463021e-01 2.409176e-01 8.099108e-01 1.114402e-01
 [421] 3.830671e-01 3.488475e-01 5.744613e-01 4.741035e-01 1.599661e-01 8.738756e-04
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 [439] 7.668372e-02 2.124649e-01 9.322893e-01 8.879287e-01 9.796764e-01 6.112825e-02
 [445] 7.302736e-01 7.319732e-01 1.615952e-01 1.056132e-01 6.383483e-01 2.189434e-01
 [451] 6.504434e-01 3.426192e-01 5.453444e-01 4.228941e-01 5.581279e-01 6.996139e-01
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 [463] 6.887198e-01 2.069769e-01 2.981673e-01 8.984941e-01 6.689006e-01 1.934649e-02
 [469] 3.456076e-01 4.451253e-01 5.947612e-01 1.385609e-01 9.177355e-01 7.136189e-01
 [475] 5.233352e-01 2.733670e-01 8.612931e-01 3.464088e-01 3.490432e-01 3.284864e-01
 [481] 2.312437e-01 5.082967e-01 8.312892e-02 6.076036e-01 3.704743e-01 1.558864e-02
 [487] 6.267360e-01 2.072939e-01 9.382966e-01 2.896143e-01 1.586536e-01 4.122598e-01
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 [505] 6.251926e-01 2.593470e-01 4.067619e-01 7.095040e-01 3.829784e-01 4.160574e-01
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 [517] 4.225827e-02 9.414318e-01 8.007753e-01 9.603588e-01 7.955735e-01 8.979139e-01
 [523] 7.346417e-01 3.838468e-01 8.020765e-01 7.358903e-01 5.071735e-01 6.551688e-01
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 [535] 4.849269e-01 6.833029e-01 1.795881e-02 5.568616e-01 3.091156e-01 1.491698e-01
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 [583] 1.237284e-05 3.424451e-01 6.278008e-01 6.184204e-01 9.418100e-01 7.678816e-01
 [589] 3.269605e-01 3.862926e-01 4.321520e-01 7.083556e-01 7.164292e-01 1.493282e-01
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 [613] 1.390632e-01 4.137804e-01 1.132903e-01 9.332350e-01 8.422440e-01 1.135942e-01
 [619] 2.508686e-01 1.998119e-01 3.896331e-01 8.897166e-01 7.412723e-01 6.076339e-01
 [625] 2.688946e-02 2.577544e-01 3.141900e-01 1.854800e-01 1.204595e-01 4.383513e-01
 [631] 9.603965e-01 4.006146e-01 3.553479e-01 6.005380e-01 1.427378e-01 1.601173e-01
 [637] 9.216962e-01 3.135525e-01 4.259694e-01 7.172462e-01 7.391820e-01 9.785578e-01
 [643] 8.801332e-01 2.015551e-01 7.635431e-01 5.647864e-01 4.565965e-01 7.353718e-01
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 [655] 6.259922e-02 4.435807e-01 7.244770e-01 6.448837e-01 8.342102e-01 6.270336e-01
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 [673] 7.857254e-01 1.965119e-02 6.949026e-01 1.759089e-01 8.038168e-01 7.861210e-01
 [679] 6.754208e-01 7.581026e-01 9.500660e-01 8.753272e-01 8.166066e-01 5.615806e-01
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 [691] 9.270653e-01 9.496268e-01 4.007208e-01 9.641884e-01 4.884434e-01 7.866077e-01
 [697] 5.841185e-01 1.809771e-01 9.392565e-01 3.922322e-01 4.967654e-02 3.385179e-01
 [703] 2.616546e-01 7.722093e-01 4.572874e-01 4.143630e-01 3.086901e-02 9.655755e-01
 [709] 7.929984e-01 2.126587e-01 3.491955e-01 4.538043e-01 7.396099e-01 8.996456e-01
 [715] 6.237158e-01 3.999175e-01 2.944106e-01 5.130748e-01 2.560212e-01 8.573716e-01
 [721] 7.777150e-01 9.447226e-02 3.440091e-01 1.108273e-01 4.493633e-01 8.470371e-01
 [727] 9.408612e-01 1.218649e-01 4.442136e-01 1.455059e-01 2.862463e-01 3.112632e-01
 [733] 7.808626e-01 5.688416e-01 7.952795e-01 2.492327e-01 6.306291e-01 1.618246e-01
 [739] 7.653637e-02 1.602250e-01 4.080635e-01 6.544335e-01 3.815755e-01 8.013844e-01
 [745] 1.934784e-01 7.404026e-01 3.240587e-01 6.513917e-01 8.896632e-01 6.426378e-01
 [751] 7.321006e-01 1.515000e-01 5.562155e-01 1.299462e-01 6.650809e-01 5.277154e-01
 [757] 6.098079e-01 8.565257e-01 8.552158e-01 9.331499e-01 3.866393e-01 7.088771e-01
 [763] 3.127253e-01 1.274529e-01 5.464589e-01 5.402559e-01 9.553517e-02 6.088726e-01
 [769] 7.760396e-01 7.616947e-01 6.529444e-02 5.051832e-02 4.033715e-01 4.123499e-01
 [775] 7.435013e-01 2.859855e-01 9.708012e-01 3.533586e-02 5.551139e-01 6.498306e-01
 [781] 9.079480e-01 7.071320e-01 3.768910e-01 7.712141e-01 2.893120e-01 3.878939e-02
 [787] 7.340990e-01 1.667828e-01 3.144339e-01 4.810597e-01 1.779767e-01 8.740828e-01
 [793] 3.896603e-01 5.624748e-01 4.106336e-01 8.493628e-02 3.251221e-01 3.300839e-02
 [799] 3.695927e-02 9.139372e-01 5.587323e-01 4.464980e-02 3.678370e-01 9.464869e-01
 [805] 2.441203e-01 5.490674e-01 8.760486e-01 4.803867e-02 5.277999e-01 2.296425e-01
 [811] 6.155400e-01 9.725531e-01 1.662431e-01 3.050384e-01 5.254774e-01 9.715387e-01
 [817] 3.317555e-01 6.149929e-01 2.566048e-01 9.678876e-01 1.279556e-01 3.088188e-01
 [823] 6.284826e-01 5.336626e-01 2.699978e-01 5.170332e-01 1.290881e-01 5.946499e-01
 [829] 5.629887e-01 6.239055e-01 6.687101e-01 8.846616e-01 3.427935e-01 8.184684e-01
 [835] 3.619581e-01 9.594710e-01 6.202046e-01 3.637331e-01 5.606857e-01 7.632958e-01
 [841] 9.175646e-01 5.926296e-02 5.379978e-01 5.228108e-01 5.994883e-01 3.150026e-02
 [847] 8.380927e-01 5.908607e-01 7.676420e-01 2.362008e-01 7.313555e-01 2.031499e-01
 [853] 4.372882e-01 4.558829e-01 7.809487e-01 8.530066e-01 2.212732e-01 5.563990e-02
 [859] 8.844864e-01 3.756724e-01 7.379396e-01 3.549684e-01 4.042711e-01 7.276540e-01
 [865] 7.376260e-01 4.161890e-01 9.380545e-02 9.776545e-02 9.480438e-01 2.117665e-01
 [871] 9.500267e-01 3.541486e-01 8.849844e-01 3.764729e-01 5.737614e-01 4.288564e-01
 [877] 4.003222e-01 4.554174e-02 9.087045e-01 2.930931e-01 1.780370e-01 7.698398e-01
 [883] 9.818542e-01 2.781551e-01 6.654788e-02 3.512905e-01 7.733384e-01 1.578835e-01
 [889] 4.355570e-01 9.000465e-01 8.449257e-01 1.715967e-01 6.447094e-01 6.883507e-01
 [895] 5.583151e-02 7.846940e-02 2.074721e-01 1.555988e-01 7.920630e-01 5.977540e-01
 [901] 5.672553e-01 4.436060e-01 7.095932e-01 3.931046e-01 5.357927e-01 4.975550e-02
 [907] 2.216311e-01 5.910991e-01 5.422630e-01 6.557732e-01 8.324503e-01 1.884154e-01
 [913] 3.001922e-01 3.440411e-02 7.886960e-02 9.370343e-01 9.223774e-01 4.101321e-01
 [919] 5.614094e-01 1.154172e-02 2.298618e-02 2.836963e-01 3.911477e-01 3.132665e-01
 [925] 8.904035e-01 8.841156e-01 6.164710e-01 6.004495e-01 3.738707e-01 4.568428e-01
 [931] 3.354594e-01 6.045625e-01 8.662286e-01 8.769950e-02 5.610604e-01 4.046860e-01
 [937] 1.352797e-01 5.654064e-01 6.577623e-02 1.932903e-01 1.133026e-01 1.372125e-01
 [943] 5.941356e-02 4.667511e-01 9.415336e-01 8.066671e-01 9.090169e-01 3.384929e-01
 [949] 2.397710e-01 6.999034e-01 4.153974e-01 5.065798e-01 7.568118e-01 7.946107e-01
 [955] 5.707690e-01 9.141142e-01 6.968190e-01 7.743342e-01 3.895301e-01 5.606482e-01
 [961] 3.488023e-01 2.110711e-01 1.090834e-01 5.572048e-01 6.966423e-01 7.371537e-04
 [967] 6.155506e-01 3.702748e-01 8.168133e-01 2.232754e-01 4.140814e-01 3.154274e-01
 [973] 8.145793e-01 8.274250e-01 4.891269e-01 1.805383e-02 1.762198e-01 9.538026e-01
 [979] 1.368064e-02 9.449145e-01 9.455823e-01 3.651386e-01 8.442138e-01 1.642109e-02
 [985] 8.320241e-01 1.979590e-01 9.639727e-01 1.938582e-01 2.851052e-01 5.916982e-01
 [991] 4.128349e-01 5.559875e-01 2.770374e-01 8.963235e-01 9.862412e-01 5.015868e-01
 [997] 1.711110e-01 1.031496e-01 1.240597e-01 2.662909e-01
 [ reached getOption("max.print") -- omitted 11897 entries ]

$adjpval
   [1] 0.52440419 0.89279215 0.96918126 0.71500137 0.92683058 0.96918126 0.85552134 0.92390705
   [9] 0.90764482 0.50957206 0.84459318 0.55550242 0.99033160 0.94191862 0.90046873 0.93428026
  [17] 0.98365938 0.96136134 0.95829409 0.32318679 0.94450938 0.98706540 0.93908978 0.98620664
  [25] 0.78999636 0.93955873 0.94191862 0.90040511 0.90492793 0.95524669 0.90877004 0.76201255
  [33] 0.68364643 0.94989500 0.76678599 0.95105638 0.92713456 0.74522893 0.88825145 0.90037895
  [41] 0.76332487 0.82215393 0.96659618 0.73983348 0.99574836 0.89433057 0.78849278 0.96918126
  [49] 0.97945173 0.84954658 0.93436330 0.98911652 0.98297565 0.99600225 0.77723210 0.94989500
  [57] 0.76332487 0.92369922 0.92920930 0.84467934 0.94989500 0.93428026 0.90070653 0.76246919
  [65] 0.96659618 0.87164469 0.91548181 0.73983348 0.67890785 0.56012964 0.99600225 0.77102540
  [73] 0.84954658 0.90972921 0.52195744 0.73983348 0.94007266 0.88404605 0.98553143 0.99670795
  [81] 0.88625333 0.96778464 0.57598811 0.85785024 0.99248735 0.92920930 0.96659618 0.94989500
  [89] 0.92364987 0.89162706 0.76332487 0.73983348 0.80526947 0.72491290 0.80171548 0.98026460
  [97] 0.97751568 0.97021732 0.85552134 0.70474043 0.92683058 0.83396455 0.99600225 0.94485384
 [105] 0.72840276 0.71821129 0.94326995 0.96659618 0.93269455 0.98396566 0.73983348 0.99931388
 [113] 0.96136134 0.94997549 0.90084961 0.85307736 0.93772744 0.88965472 0.88825145 0.91419625
 [121] 0.99033160 0.97597710 0.76332487 0.94989500 0.91871704 0.80797901 0.80108338 0.97777321
 [129] 0.95843040 0.96960851 0.89149144 0.96918126 0.89078115 0.97597710 0.84430488 0.87677579
 [137] 0.92120385 0.77252268 0.92683058 0.91871704 0.90841074 0.98725551 0.93428026 0.84467934
 [145] 0.89279215 0.89286469 0.96659618 0.92920930 0.58090325 0.92717524 0.98725551 0.85552134
 [153] 0.84954658 0.92920930 0.91710102 0.73983348 0.93269455 0.90356135 0.97455342 0.61141993
 [161] 0.74573792 0.69222715 0.98026460 0.96466729 0.55550242 0.74573792 0.99033160 0.99217648
 [169] 0.73826078 0.76724823 0.83396455 0.61141993 0.96579612 0.88191763 0.98725551 0.98239800
 [177] 0.91035691 0.73498408 0.84954658 0.79534583 0.97597710 0.73983348 0.84776838 0.72808184
 [185] 0.98297565 0.84954658 0.98836922 0.94989500 0.97815524 0.97225660 0.85662176 0.90037895
 [193] 0.84430488 0.56027293 0.93436330 0.92364987 0.95105638 0.89286469 0.80970596 0.85662176
 [201] 0.93376462 0.99600225 0.85552134 0.91710102 0.92920930 0.96094409 0.76332487 0.86381823
 [209] 0.96709810 0.77906166 0.89279215 0.73983348 0.96990995 0.89241398 0.92550037 0.89294385
 [217] 0.93436330 0.97022562 0.99033160 0.96960851 0.92881111 0.78121570 0.92734270 0.99574428
 [225] 0.98911652 0.71821129 0.98735849 0.76724823 0.90764482 0.91419625 0.91328750 0.92488656
 [233] 0.88518253 0.79534583 0.99550697 0.84690914 0.92006215 0.93139884 0.93269455 0.96136134
 [241] 0.99438435 0.58009205 0.56327473 0.90764482 0.88625333 0.86628174 0.94201918 0.90421186
 [249] 0.61825042 0.90750441 0.92981755 0.95524669 0.98706540 0.96074204 0.65128896 0.92390705
 [257] 0.94989500 0.88625333 0.97319768 0.97455342 0.84954658 0.96659618 0.96659618 0.96603648
 [265] 0.90702881 0.96690825 0.96094409 0.94989500 0.74687631 0.16053347 0.86381823 0.97569808
 [273] 0.98628701 0.96136134 0.79145833 0.99730625 0.85307736 0.84954658 0.87983656 0.75937422
 [281] 0.88625333 0.99033160 0.98239800 0.87802341 0.92683058 0.82366058 0.92713456 0.99600225
 [289] 0.98938039 0.89241398 0.92369922 0.93198595 0.91505811 0.81789021 0.96659618 0.99406023
 [297] 0.87677579 0.95570099 0.83382632 0.93436330 0.97382890 0.90764482 0.96659618 0.91283290
 [305] 0.95570099 0.73983348 0.84954658 0.91932781 0.96053842 0.96918126 0.96659618 0.92364987
 [313] 0.55143345 0.69365196 0.90972921 0.84525754 0.81861836 0.98975865 0.95338764 0.99574428
 [321] 0.96659618 0.89139325 0.76321608 0.95072121 0.72840276 0.90356135 0.96603648 0.96136134
 [329] 0.96427395 0.99574428 0.92511571 0.76575832 0.72831489 0.90972921 0.80779157 0.72034206
 [337] 0.95524669 0.92511571 0.97597710 0.92364987 0.80797901 0.96918126 0.97455342 0.80779157
 [345] 0.85307736 0.15585175 0.85660096 0.98330502 0.80446373 0.97022562 0.89739928 0.89149144
 [353] 0.94665099 0.90841074 0.96659618 0.94201918 0.98040526 0.97790238 0.99600225 0.94201918
 [361] 0.72034206 0.96960851 0.88825145 0.53565961 0.98911652 0.99574428 0.94201918 0.78444662
 [369] 0.94926410 0.84381453 0.92938872 0.64366820 0.73826078 0.96659618 0.71719276 0.73826078
 [377] 0.76724823 0.85647951 0.61083471 0.96466729 0.61801385 0.89279215 0.92920930 0.99574836
 [385] 0.56835783 0.84776838 0.96136134 0.84635875 0.96659618 0.99600225 0.76321608 0.96603648
 [393] 0.20579187 0.84467934 0.84938121 0.98866978 0.95570099 0.96918126 0.92920930 0.78020587
 [401] 0.96933341 0.98706540 0.95654615 0.98655236 0.99574428 0.98808580 0.92364987 0.97597710
 [409] 0.94989500 0.86628174 0.96802170 0.99574428 0.76724823 0.94989500 0.83382632 0.95524669
 [417] 0.89504087 0.85552134 0.97597710 0.76724823 0.90764482 0.89739928 0.94665099 0.92724817
 [425] 0.80970596 0.17339037 0.87936691 0.90750441 0.64321996 0.95843040 0.95105638 0.96136134
 [433] 0.87802341 0.80630401 0.87708773 0.98365938 0.96427395 0.78121570 0.73826078 0.84938121
 [441] 0.99249315 0.98706540 0.99600225 0.71099089 0.96659618 0.96659618 0.80970596 0.76332487
 [449] 0.95338764 0.84954658 0.95531998 0.89286469 0.94191862 0.91714596 0.94350704 0.96212468
 [457] 0.90084961 0.74060640 0.74522893 0.95014213 0.78889384 0.87677579 0.96136134 0.84737522
 [465] 0.88150904 0.98836922 0.95829409 0.56012964 0.89468103 0.92299373 0.94989500 0.79534583
 [473] 0.99033160 0.96466729 0.93436330 0.87665020 0.98297565 0.89504087 0.89739928 0.89078115
 [481] 0.85316301 0.93428026 0.73983348 0.94989500 0.90492793 0.54426323 0.95052684 0.84737522
 [489] 0.99434766 0.87802341 0.80939689 0.91419625 0.96918126 0.90948050 0.94463787 0.89162706
 [497] 0.83396455 0.93090267 0.98748428 0.96918126 0.96136134 0.92998932 0.94960002 0.83684137
 [505] 0.94997549 0.86585499 0.91357642 0.96462062 0.90764482 0.91443288 0.56835783 0.79786953
 [513] 0.90764482 0.88519210 0.85316301 0.98239800 0.66141372 0.99514633 0.97480076 0.99574428
 [521] 0.97319768 0.98812890 0.96659618 0.90764482 0.97480076 0.96659618 0.93383635 0.95584981
 [529] 0.94827850 0.94989500 0.97021732 0.98261282 0.87523104 0.73983348 0.92920930 0.96136134
 [537] 0.55550242 0.94339013 0.88518253 0.80446373 0.86712344 0.98238323 0.96659618 0.76724823
 [545] 0.92364987 0.78999636 0.98472112 0.98239800 0.85316301 0.94201918 0.89286469 0.90492793
 [553] 0.64321996 0.90025929 0.99574836 0.84690914 0.97022562 0.92666296 0.98725551 0.71821129
 [561] 0.72214112 0.76911628 0.96136134 0.87802341 0.98203855 0.99813163 0.92920930 0.99574428
 [569] 0.94960002 0.55550242 0.94326995 0.94472181 0.72034206 0.85552134 0.81789021 0.94980286
 [577] 0.99722621 0.93160571 0.92920930 0.94827850 0.85785024 0.92102828 0.02531949 0.89286469
 [585] 0.95072121 0.94989500 0.99514633 0.96960851 0.88999781 0.90764482 0.91871704 0.96438954
 [593] 0.96531365 0.80446373 0.90841074 0.95745136 0.98949285 0.93198595 0.90025929 0.97569808
 [601] 0.76332487 0.88825145 0.74573792 0.99550697 0.98026460 0.90084961 0.98185659 0.05011791
 [609] 0.87802341 0.96136134 0.97225660 0.99574428 0.79534583 0.91419625 0.77025951 0.99249315
 [617] 0.98124854 0.77037508 0.85785024 0.84467934 0.90841074 0.98706540 0.96659618 0.94989500
 [625] 0.59485595 0.86412672 0.88625333 0.83611852 0.78012432 0.92030222 0.99574428 0.91035691
 [633] 0.90037895 0.94989500 0.80108338 0.80970596 0.99033160 0.88625333 0.91804157 0.96578870
 [641] 0.96659618 0.99600225 0.98553143 0.84467934 0.96918126 0.94485384 0.92511571 0.96659618
 [649] 0.98472112 0.92364987 0.92082994 0.99742369 0.90841074 0.54426323 0.71500137 0.92188710
 [657] 0.96659618 0.95524669 0.98026460 0.95056645 0.99574428 0.87677579 0.71719276 0.84467934
 [665] 0.85307736 0.98747479 0.98330502 0.97209469 0.91422673 0.99535976 0.92724817 0.77025951
 [673] 0.97150277 0.56012964 0.96136134 0.82920205 0.97563506 0.97150277 0.96053842 0.96918126
 [681] 0.99558689 0.98490774 0.97738765 0.94463787 0.96659618 0.87665020 0.96659618 0.98627946
 [689] 0.99574428 0.98297565 0.99206447 0.99550697 0.91035691 0.99574428 0.92926022 0.97164978
 [697] 0.94926410 0.83270127 0.99475257 0.90877004 0.68364643 0.89279215 0.86678945 0.96960851
 [705] 0.92511571 0.91419625 0.61549001 0.99574428 0.97245421 0.84938121 0.89739928 0.92390705
 [713] 0.96659618 0.98836922 0.94989553 0.90997468 0.87934544 0.93428026 0.86381823 0.98261282
 [721] 0.97021732 0.74573792 0.89323253 0.76724823 0.92364987 0.98185659 0.99514633 0.78020587
 [729] 0.92210250 0.80171548 0.87708773 0.88568119 0.97022562 0.94613754 0.97319768 0.85785024
 [737] 0.95105638 0.80970596 0.73826078 0.80970596 0.91383833 0.95570099 0.90764482 0.97480076
 [745] 0.84430488 0.96659618 0.88825145 0.95531998 0.98706540 0.95524669 0.96659618 0.80526947
 [753] 0.94337180 0.78902105 0.95826536 0.93488396 0.94989500 0.98261282 0.98239800 0.99249315
 [761] 0.90764482 0.96462062 0.88625333 0.78889384 0.94201918 0.94083685 0.74719044 0.94989500
 [769] 0.97021732 0.96918126 0.71913102 0.68466885 0.91247498 0.91419625 0.96659618 0.87708773
 [777] 0.99594444 0.62428306 0.94326995 0.95531998 0.98911652 0.96390104 0.90636336 0.96960851
 [785] 0.87802341 0.64321996 0.96659618 0.81686125 0.88625333 0.92920930 0.83073180 0.98478256
 [793] 0.90841074 0.94472181 0.91388186 0.73983348 0.88855692 0.61814328 0.64147782 0.99033160
 [801] 0.94380751 0.67260782 0.90356135 0.99550697 0.85647951 0.94207038 0.98490774 0.68008202
 [809] 0.93488396 0.85316301 0.94989500 0.99600225 0.81615431 0.88290376 0.93436330 0.99600225
 [817] 0.89153087 0.94989500 0.86381823 0.99574836 0.78889384 0.88518253 0.95072121 0.93827387
 [825] 0.87272218 0.93432973 0.78889384 0.94989500 0.94472181 0.94989553 0.95829409 0.98655236
 [833] 0.89286469 0.97777321 0.90084961 0.99574428 0.94989500 0.90195451 0.94450938 0.96918126
 [841] 0.99033160 0.70651734 0.93957585 0.93436330 0.94989500 0.61549001 0.98040526 0.94989500
 [849] 0.96960851 0.85552134 0.96659618 0.84492460 0.92006215 0.92511571 0.97022562 0.98239800
 [857] 0.84954658 0.69804254 0.98655236 0.90521416 0.96659618 0.90037895 0.91283290 0.96659618
 [865] 0.96659618 0.91444679 0.74573792 0.75501860 0.99550697 0.84818397 0.99558689 0.90025929
 [873] 0.98655236 0.90634708 0.94665099 0.91871704 0.91025311 0.67376247 0.98911652 0.87909252
 [881] 0.83073180 0.96960851 0.99618620 0.87677579 0.72034206 0.89867198 0.96990995 0.80797901
 [889] 0.92006215 0.98836922 0.98157879 0.82215393 0.95524669 0.96136134 0.69937073 0.73826078
 [897] 0.84751570 0.80706241 0.97225660 0.94989500 0.94533815 0.92188710 0.96462062 0.90877004
 [905] 0.93908978 0.68364643 0.84954658 0.94989500 0.94191862 0.95627135 0.98019402 0.83975497
 [913] 0.88191763 0.61997274 0.73826078 0.99417822 0.99033160 0.91388186 0.94463787 0.51865333
 [921] 0.57045237 0.87708773 0.90841074 0.88625333 0.98706540 0.98655236 0.94989500 0.94989500
 [929] 0.90492793 0.92511571 0.89241398 0.94989500 0.98330502 0.74222553 0.94463787 0.91322118
 [937] 0.79376785 0.94485384 0.71934764 0.84430488 0.77025951 0.79534583 0.70720158 0.92683058
 [945] 0.99514633 0.97597710 0.98911652 0.89279215 0.85552134 0.96212468 0.91419625 0.93376462
 [953] 0.96918126 0.97304349 0.94665099 0.99033160 0.96136134 0.96990995 0.90841074 0.94450938
 [961] 0.89739928 0.84776838 0.76693423 0.94339013 0.96136134 0.16286312 0.94989500 0.90492793
 [969] 0.97749292 0.85044736 0.91419625 0.88625333 0.97691106 0.97910820 0.92938872 0.55550242
 [977] 0.82975805 0.99574428 0.54426323 0.99514633 0.99528290 0.90250142 0.98157879 0.54426323
 [985] 0.98005434 0.84467934 0.99574428 0.84430488 0.87708773 0.94989500 0.91419625 0.94329711
 [993] 0.87677579 0.98768658 0.99722567 0.93243385 0.82215393 0.76332487 0.78354454 0.86854934
 [ reached getOption("max.print") -- omitted 11897 entries ]
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGlzdChpbnZub3JtY29tYiRyYXdwdmFsLCBicmVha3M9MTAwLCBjb2w9XCJncmV5XCIsIG1haW49XCJJbnZlcnNlIG5vcm1hbCBtZXRob2RcIiwgeGxhYiA9IFwiUmF3IHAtdmFsdWVzIEVjb3R5cGUgKG1ldGEtYW5hbHlzaXMpXCIpXG5gYGAifQ== -->

```r
hist(invnormcomb$rawpval, breaks=100, col="grey", main="Inverse normal method", xlab = "Raw p-values Ecotype (meta-analysis)")
```

<!-- rnb-source-end -->

<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABgQAAAO3CAYAAADs8i97AAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAAA4ZVhJZk1NACoAAAAIAAGHaQAEAAAAAQAAABoAAAAAAAKgAgAEAAAAAQAABgSgAwAEAAAAAQAAA7cAAAAAOStg1wAAQABJREFUeAHs3Qm0HEW9OOBfCCGBhIQlrCEJiSyBsASJEEFkk7/IYmTxvUNAQBAVVJRF1IfwADksLqDIokcREAMRkOWxqegxrBK2RFljAIkJLsRgyEI2kvlP9Xszzr0zcxdyc+/c21+fM053dXV31Vc9l1i/7qpeheISFgIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBHC6zRo2uncgQIECBAgAABAgQIECBAgAABAgQIECBAgEAmICDgRiBAgAABAgQIECBAgAABAgQIECBAgAABAjkQEBDIQSOrIgECBAgQIECAAAECBAgQIECAAAECBAgQEBBwDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRwICAjkoJFVkQABAgQIECBAgAABAgQIECBAgAABAgQICAi4BwgQIECAAAECBAgQIECAAAECBAgQIECAQA4EBARy0MiqSIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBATcAwQIECBAgAABAgQIECBAgAABAgQIECBAIAcCAgI5aGRVJECAAAECBAgQIECAAAECBAgQIECAAAECAgLuAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAMBAYEcNLIqEiBAgAABAgQIECBAgAABAgQIECBAgAABAQH3AAECBAgQIECAAAECBAgQIECAAAECBAgQyIGAgEAOGlkVCRAgQIAAAQIECBAgQIAAAQIECBAgQICAgIB7gAABAgQIECBAgAABAgQIECBAgAABAgQI5EBAQCAHjayKBAgQIECAAAECBAgQIECAAAECBAgQIEBAQMA9QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEciAgIJCDRlZFAgQIECBAgAABAgQIECBAgAABAgQIECAgIOAeIECAAAECBAgQIECAAAECBAgQIECAAAECORAQEMhBI6siAQIECBAgQIAAAQIECBAgQIAAAQIECBAQEHAPECBAgAABAgQIECBAgAABAgQIECBAgACBHAgICOSgkVWRAAECBAgQIECAAAECBAgQIECAAAECBAgICLgHCBAgQIAAAQIECBAgQIAAAQIECBAgQIBADgQEBHLQyKpIgAABAgQIECBAgAABAgQIECBAgAABAgQEBNwDBAgQIECAAAECBAgQIECAAAECBAgQIEAgBwICAjloZFUkQIAAAQIECBAgQIAAAQIECBAgQIAAAQICAu4BAgQIECBAgAABAgQIECBAgAABAgQIECCQAwEBgRw0sioSIECAAAECBAgQIECAAAECBAgQIECAAIE1ERAgQIAAAQIECEQsW7YsnnjiiZoUH/jAB2qmSyRAgEBHCjz22GOxcuXKJqdcZ5114r3vfW+TNBurX+DRRx+NQqHQ5EIDBgyIMWPGNEnryRsvvfRS/POf/6yq4vve977o27dvVboEAgQIECBAoHsI9Cr+I6fpv3K6R7mVkgABAgQIECDQoQKzZ8+OoUOHVp2zV69eVR10VZkkECBAoAME1l577ViyZEmTM+2www7x7LPPNkmzsWoCv/rVr2K33XaL9ddfv+6JevfuXfW3f5dddolnnnmm7jE9bceRRx4Zv/jFL6qqNXPmzBg2bFhVugQCBAgQIECgewgYMqh7tJNSEiBAgAABAgQIECBAgMAqCLz88svx0Y9+NA488MBYuHDhKpzJoQQIECBAgACB7isgINB9207JCRAgQIAAAQIECBAgQKAVgQULFsRXvvKVGD16dNx9992t5LabAAECBAgQINCzBcwh0LPbV+0IECBAgAABAgQIECCQW4Hbb789Pv/5z8ff/va33BqoOAECBAgQIECgUsAbApUa1gkQIECAAAECBAgQIECgxwhcd911ggE9pjVVhAABAgQIEOgIAQGBjlB0DgIECBAgQIAAAQIECBAgQIAAAQIECBAg0OACAgIN3kCKR4AAAQIECBAgQIAAAQIECBAgQIAAAQIEOkLAHAIdoegcBAgQIECAAIEWBF599dV4+eWX44033oitttoqdt5551h77bVbOKLrdy1atCj+8Ic/ZOVef/31Y7PNNsvK3adPnw4t3MqVK+Opp56KZPSe97wndtxxx+jXr1/daxQKhWz4j7/85S+RPu+8805suumm5c8GG2xQ99j27Ois+renTKW8q+t+Sravv/565jpr1qxYsWJFbLzxxrHllltm923p+h313d62r3XdN998M7tPU7n79+8fw4cPjzFjxsQaa9R/7mnOnDnxxz/+Mf7617/GeuutF1tssUXstNNO0bt371qXaDUtuf3zn//MzpfOmX7n6fed7sX02xk1alRWtlZP1CAZGsE0UTTyb7Cyqf785z/HSy+9FHPnzs0mLd5hhx2iI/5OdsbvMf1unnnmmVhrrbVi9913z34PlXWzToAAAQIECPRQgeI/NCwECBAgQIAAgdwLFDtAC8V/7lV9evXqVdOm2FlaKHZEV31+/vOfl/Nff/31hbFjx1ads9jxWCh2GhWuvvrqct7mK1/5yleqzp2u973vfa951prbxQBEzeMPPPDAmvlTYrFTs3DSSScVtttuu0KxQ7Wq3AMHDiz853/+Z2HatGl1z1G5o9jRVLMM06dPLxQ7gwtnn312YcMNN2xyncGDBxcuuOCCytNk68WgQeHggw8uFDuumuRv3ma77LJL4Zprrim89dZbVedoLaGj69/a9Sr3r+77qfJazddnzpxZOO+88wrFzvS6tsWgQOFLX/pSodiB2Pzwmtur2vY33nhj1b2zzTbblK/13HPPFQ477LCa90Oqx3e/+93sHisfUFx59tlnCx/96EcLa665ZlU9i0GB7L5btmxZ5SEtrv/rX/8qXHbZZYViIKvqfJX3Zd++fQsHHHBA4YYbbqgqU/MLFINhVedKfys6YukOph31G7ztttvK90/yr2yP0vpGG21UzpP+thYDIU2Ya/0NTH9fSsutt95a8+97asNi53ph4sSJpazt+l4dv8fKArzyyiuFj33sY4VNNtmkiUv6b932229fOOeccwrLly/PDjniiCOa5CnZpTJaCBAgQIAAge4rEN236EpOgAABAgQIEOg4gXcTECh1jlR+pyBA8QnkQup4r0yvt3744YdXdUSlWj355JM1jx89enSbKp06dWpd86qrrqp5/G9+85vC5ptvXvOY5udJHWWf+9znyp1GNU9YTHz88cdrnu/555/Pjm9+3tL2+PHjy6dMna5pu7Svrd/Fp7Mzw/KJWllZHfVv5ZJNdqeAQK26ddT91ORi/7eRrpmCLylAVevatdKKT75nx6SATkvLqrb9D3/4w6oyDRo0KLtk6oRvLTCUyp4CBqWOzcsvv7xNx+y9996FdM+1tjz00EOFddddt6qMtcwq0/bZZ59C8UnyuqdfnQGBRjftyN9gCn5UurdlPQUjKpd6AYHimwvZvdWWc37+858vtDXItDp/j6V6TZo0qZACu62V/YMf/GDh73//e0FAoCTnmwABAgQI9CwBAYGe1Z5qQ4AAAQIECLxLgY4KCKQn+N/73ve22uFS2SFz0EEH1Sx1vfM8/fTTNfOXElNnba2nvddZZ53CvHnzStnK3//1X/9VSE+HVpapLeuHHHJIYfHixeXzNF+p1ymcOqFbOv8tt9ySnSrVI12jpbwt7UsdyFOmTGlerKrt1VX/qgu1kFAvINCR91Pl5dPT0Omp9Zb8WtqXAllvv/125SmbrK9q29frvL7wwgvbVebUthdffHG7jjn++OOb1KX5RgrWtaVTtZ7f/vvvX0jtXWvpioBAI5h29G9wdQUE0lsqqbO8XtvWSj/22GNrNXWTtNX9e0wXO+OMM9pV7hQgTgHoWnXyhkCT5rNBgAABAgS6nYCAQLdrMgUmQIAAAQIEVodARwUEiuOY1+xAqdWpUplWHNO8qlq1OkXTMaeeempV3sqE3/3udzXLUKuj87777quZt7JsLa2nTqZ6S71O4VpDtpSukTpaS0GGb37zm3XLlob4KI77XiiOF1817FDpXOk7DbfS0tPsq7P+9VxqpdcLCHTk/VR53TR0TqXTu1k/7bTTKk/ZZH1V277evd/ecr6bQFe6xtSpU5vUp7SR2mnEiBGrbPfYY4+VTtnku7MDAu31TPk72nR1/AZXV0Dg3Xilv3ezZ89u0s7NN1b37zEFsd5tu9Wqs4BA8xa0TYAAAQIEupdA/dm2iv/ltxAgQIAAAQIECLRPIE2EmZbiuNVx+umnZxM2pklG/+d//ieOPPLIuie78sorq/ZNmDAhisOSVKXffPPNURwKpSq9lFAcZqa02uT705/+dJPtdI5ip26TtNLGvvvuG/fff382gW+xczSKT/VnE0+W9pe+i0OxxBNPPFHabNN3mgi4tBQ7qbIJa5NXWopPnpcnFS6OwV3KVv7eddddozjkUFauNOlxKltxqI/41a9+FcU3Asr5SivFseazepS2K7+7qv6VZWhtvSPvp9K1inNXZPdjabvyO02y+4lPfCKuuOKK7HPMMcfUdE3HFN9eiGLHf+Xhra63te1bO9H73//+rHxpsu7iWyBRHJql5iHF/2tWTk+TB5977rnZJMRpEth0T6f7r9byy1/+slZy5vbn4iSyzZfik+NRDGBlv4U0ueyLL74YP/rRj2Lo0KHNs2bbxU7wmuldmdgVpqvrN1h8uj2Kw7Zln+JcATVZi8M3lfOkvGli3bYu6e9V+ntanKsgm4g73S+f+cxnah6e7vn0m6u3dMbv8cwzz0wPAlYVId3/X/jCF6IYRI5//OMf2XdxrpC6v4uqE0ggQIAAAQIEuqdA94pfKC0BAgQIECBAYPUIdNQbAsV/EWZPD//4xz+uKmh6urjY0Z7tL+UrfadJKGstxU6mmvnvuuuuWtkLCxcuLAwYMKDqmFoTk37nO9+pypfKc+KJJ9Yc0iQ91VxrvPli4KJmWeo9JV6qc7FztvCXv/wlO3bp0qWFH/zgB4XSk9OpHrWu9Ytf/KLmtVJiGm+/dO40kWiaAPSTn/xk4d577615zOquf82L1kms94ZAqT4ddT+lIaPS0FGl81Z+v+997ysUO7urSpgmIa03fFWahDS1XfNlVdo+naulNwR23nnn8lsklddtaQik9OR9mlS4+fLlL3+5pkUxeNc8a7b98Y9/vCp/mq/ir3/9a838aa6BSuPSepqcu9bSVW8IdJVpZ/wG6w07VvrbU6sdUlqtOQRK7VcM9tQ8LP3tLOWp/E5/92stnfF7LAaja5Yp/X2t9/f0jjvuqDn5dqlO3hCo1ZrSCBAgQIBA9xEwZFD3aSslJUCAAAECBFajQEcGBIpPVdctafGp9pqdM8Wnl2se88wzz9TMnyZ7rLVUdoqXOm/SdxqLvnJJw+gUnwavOveGG27Y4qSqaZLMyvOm9dSJWWtugpY6hVsySuWcMWNG1XXStdJkzbWuVarbnXfemXX8Fp88LiXV/O6M+te8cJ3ElgICLVm1934qPo1c0zWNF54mS623LFiwoLDJJpvUPPbb3/521WGr0vbpZPUCAmlS4+LT/VXXSwn17v1a93/pBL///e9r1ulDH/pQKUvVd5o0PE2AmyY3TsNw1ZuoOx2Y7rNanfzpPq611MpbK5hX69jW0hrNtLN+gx0dEDjhhBPqUqfAUPO/j2n7Pe95T81jOuP3mIJbtcpUawi5ykIeffTRNY9L5xIQqJSyToAAAQIEup+AIYOK/6KxECBAgAABAgQ6UuCcc86pe7rtttsu+vTpU7W/OG5+VVpKKD7lHrvttlvVvnvuuSeKE1FWpd9www1VacVOxmwYmModxTGto9ixXpmUrY8fPz7SsDH1lmInUdWuJUuWxCOPPFKV3lJCvSFeSscUO9Ci+OR1abP8nYbm2GyzzaIYEMmGZHn11VfL+9JKKn+xAzWK43Y3SW++0dX1b16elrY78n669tpra17qG9/4RhTfHKi5LyUW3zqJ//7v/665/9FHH62ZXi+xtbavd1xK33PPPWPbbbetmWXkyJE109Pv7bOf/WzNfcX5AGqmz58/v2Z6Shw8eHAUJwbOhtu67rrr4pRTTqmb929/+1vNYb9q/fbqnmQ17+gq0+70G6xsguIcLpWbTdbT36Zaw7wV33hqkq+00Rm/x+Z/I9O101BBafislpavfOUrLe22jwABAgQIEOjGAgIC3bjxFJ0AAQIECBBoPIHU+Z46s+stqXNy/fXXr9pdHHalKq2UUGts6pT/5z//eSlL9l18ajMmT57cJC1tFIc5qbrmn/70p6p8KaGlsqf9o0aNSl9VSxrPv61LceLgmkGOyuNTh9Uee+xRmVReT8GT22+/PRvDO5V3yJAhWcAjebz11lvlfC2tdGX9WypX830deT+lwE3xjZPml8jmbDjuuOOq0psnFIdDieIwI82TozgUT1VavYS2tH29Y1N6cWiburuLbw/U3Fcc1qju+PCpPLWWln6PtfKX0tJ8IXfffXd8/etfj+IwYJHmLSi+UVDaXf5+t+cvn6ADV7rKtLv8Biup0z2WAo4tLbUCArXau7N+j8Uhv6qKW3zbJ+oFw0qZd9xxx6r/bpT2+SZAgAABAgS6t0DLj05177opPQECBAgQIECg0wVSR0utTtPKgqRO3uZL8UXT5knl7eJ449kExc07u3/605/GySefXM6Xtmud56STTirnKa0Uh+QprTb5Lg5/EjfddFOTtOYbqbO++XXSRL9tXVInfjpHa0txfPd44IEHolZnWuWxxWE64mc/+1n2SW8G7LXXXtkEn//xH/9R9zpdWf/Ksre23pH3UwoYNW+3dP3UMdjaPZvypUlXhw0bFsV5BtJmeUlPIL/99tstvmFQytzWti/lb/5dHNKqeVJ5u9bvKu1MhvWW1MGb6l4csqlelhbTk2ma0Prhhx+O9KZEc5t6BxfHp6+3q9PTu8q0u/wGKxskWbX2WylNkF55XJo8ufnSGb/H9BZZ8/9upHIMHz68eXFqbqdg67/+9a+a+yQSIECAAAEC3VdAQKD7tp2SEyBAgAABAg0oUO8p5cqi1uu4rMxTud6/f/8ojiMfqbO+cimO0x7pKdttttkmS04BgeZLGqIodZA3X+o9nZs619OnvUt7Oo023XTTNp3+gx/8YEyaNCmKY2C3ucP2nXfeid/97nfZ5/LLL4/vfve7MW7cuKrrdWX9qwrTQkJH3k/FSVRrXqneUDu1MqcO/ead3sWx4CMFhIqTEtc6pElaW9u+yUEVG+m3UG+pN0xUGu6opSV1zrc3IDBlypT46le/Gg8++GDNIEtL10v7WutUbu34jtzfVabd5TdYaV3vjZLKPLUCApX7S+ud8XusNVxQun7q6G/LkvI999xzbckqDwECBAgQINCNBBrn0ZRuhKaoBAgQIECAAIF6Am3pwH03nYGf/vSna15y4sSJWXpxctR4+eWXq/LUejsgZar11GjVwe1ISE+It3WpNaRGvWM/9rGPxb333hs77bRTvSx101OnbRrr/cUXX6zK05X1rypMCwkdeT/VmyOgPfdjvTc7ihMSt1CLf+9qT9v/+6h/r7XF49+5/3etXr2b52vrdpq/Y7/99suG56r1xkXpPOlp8vR2z6BBg0pJ5e9GekOgq0y7y2+w3GjFlbZ09tf7jVSeJ63Xuy878vdY71z15jRoXsZa8900z2ObAAECBAgQ6H4C3hDofm2mxAQIECBAgEADC6yuDpTUIf7+978/Usd/5fKLX/wizj///Ljlllsqk7P11Hl17LHHVqWnhHpDRrz3ve+NjTbaqOYxLSXWm1ug1jEtPZFcK/+HP/zhSJ/UwZ8CIKlDtvlT6rWOS2kpUJE6ZadNmxaVnbBdWf96Za2V3pH30+abb17rElHvKeJamevlbes90962b16Gtna2Vh7X2jEtdepXnietp6Gpjj/++JpvFGy88cbxoQ99KPbdd9/4wAc+kE1+nK6dhllq3vldeS82v0Znb7fmU6s8rR3TFtPu8husVf+W0tpS93R8Z/we0xs9tZZa81rUylfvLYZaeaURIECAAAEC3UdAQKD7tJWSEiBAgAABAj1EoK0dRs2rmyYXbh4QSEO1pKE30iS7zZfDDz886o0PXm+YmE996lNN5iVofs6O2H63ndxpktb0ueKKK2L69Onxm9/8Jhu7/ZFHHonXX3+9btHSpLdpYs2tt966nKcr618uRAettPV+aqkDMp2jtU7eNKxOrQ7C9BTy0KFD21Sbd9v2bTr5as6U5rI4/fTTq4IBqXP/yiuvjPTbqVW/ZcuWVZWs3pPbVRl7cEJX/wbb+rtZXU3QGb/HNMRRCtY1DwC0dVi4Wr/31eXhvAQIECBAgEDnCRgyqPOsXYkAAQIECBAgsEoCaZLc9ddfv+ocZ599ds2O2nrDDKUTpIlkay0vvPBCreQOTXu3naHz5s0rl2PbbbeNz33uc9kcA7Nnz86CIhdffHHUG5LmD3/4Q/nYtNKV9W9SkE7cSG+MJLfmS3qL4rbbbmueXLWdJpuuNTlqmjugLWOrpxO+27avKkwXJPz2t7+t6lhNxTj11FOzIFqtYEDqdJ4/f35VaRvpDYGqwnVSQmf9ButZp7kvunLprN/jVlttVVXNf/zjH/HEE09UpVcmPPnkk1H5N7dyn3UCBAgQIECgewsICHTv9lN6AgQIECBAIEcCaazvWkMA1erMTRMN77333nV1tt9++6g1uXEagmjBggV1j/vmN78Z6dg00e9///d/Z0MVpbcUanUU1ztJvQ66yvx33nlnXHTRRXHcccdlkwKnQMgmm2wSb775ZmW28np6+j9N8nrKKaeU0ypXFi9eXLmZ1aGr6t+kIJ28kSanrrV87Wtfa7EN09Px55xzTq1D49BDD62ZXiuxLW1f67hGSEtvmdRaxo4dWys5S5s6dWo0v/fSjjT5dd6XzvobVG+y6fb8zVpdbdUZv8fKN6Mq6/GjH/2ocrNq/YILLqhKk0CAAAECBAj0DAEBgZ7RjmpBgAABAgQI5ESgpaf+KwnS8CUtDQGTOthPPPHEykOy9b/97W/xX//1X1XpKSE9Lfr9738/m6Q3BQ5Sh1Ean3+HHXZo9WnTmidsITGN1Z7efPjpT3+azR2Qrp2GXvnWt77VwlERs2bNqrl/3LhxTdIbvf5NCtuBG6kDstZ9kTq7x48fH//85z+rrpaGG0md/jNnzqzat95669UNwlRl7uYJa621Vs0a1HvSesmSJVlAq9ZBKcCS96WzfoO13txI9m0dNmd1tlNn/B7rBUl//OMfRwrw1lrSEFhprhYLAQIECBAg0DMFBAR6ZruqFQECBAgQINBDBdJTtXvttVeLtUsdl2ni09aWs846q+aY56kz6LDDDouXX345O0V6wjk9sX/QQQdFGp6n+ZIm/N1zzz2bJ6/S9hlnnFHz+EsuuSTOPffcqmFY0tO+KXjw85//vOq4wYMHN5k/oJShketfKmNHf2+55ZZxwgkn1Dzt/fffH2PGjImvfOUr2RBCt956aySjlPbAAw/UPCa9NZCCAnlYdtlll5rVvPrqq+N//ud/muxLgamjjjoqnnvuuSbppY2W3sIp5cnDd2f8BlPgodaShhxLT8mnwOYBBxwQb7zxRq1sqzWtM36Pad6VFLittaTfenrrbNKkSTFjxoy4++67IwUpvvCFL9TKLo0AAQIECBDoIQImFe4hDakaBAgQIECAQH4E0uTCDz/8cN0Kpye900SSrS3Dhg3LnmBOT4o2X1IAIH1SZ2/qbF+0aFHzLNl2GgLmG9/4Rs19q5L4/ve/Pz7ykY9E6qRuvqTrXXbZZVknfxpGKD3V/uqrr8a//vWv5lmz7UsvvbRmeiPXv2aBOygxveWRnmpPky03X9LkzPWeGm6eNw0bddpppzVP7rHbO+64YzZXQvM5AdLwP+k3l95CSUN1pTcpHn/88WjpLYB6b7L0WLw6FeuM32Bqk1pLGuqs8o2rNIHuxhtvXCvrak3rjN9jml/ljjvuyN6yal6ZG2+8MdKn1pLermiEoZVqlU0aAQIECBAg8O4FvCHw7u0cSYAAAQIECBDoEoHUEbvhhhvWvXZlJ1fdTP+344orrmhxroE0VE+9YECaIDYN7ZMmlV0dS5obYd9996156lSmadOmxa9+9at4+umn6wYDPvvZz9Z9Ij6duJHrX7PiHZCY5qJIQz7VG1u8LZdIb4tcf/31NYcfasvx3TFPcrvqqqvqFj0FAdJbKg8++GCTYMDIkSOrjkkTOf/xj3+sSs9jwur+DabJ2Nsyd0Wtt586oz064/eYJnBOb1fVGi6sXh3TmxXf+c536u2WToAAAQIECHRjAQGBbtx4ik6AAAECBAjkU6Bv3751xyZ/z3veE/vvv3+bYVJnVBomot6QEvVOlCbkvfnmm7NhUerlWdX0ddZZJxvH+vDDD2/3qVL5Tj/99Pje977X4rGNXP8WC76KO1MwIE14m942ac+SJmhNw4yke6Z///7tObRH5E3DqbQ14JaCdmkIq1pvuSSMiRMn9giTVa3E6v4NDhkyJJsEvbVyduVbG53xe0xv89x7772xwQYbtEYRQ4cOjfvuuy+bfL3VzDIQIECAAAEC3U5AQKDbNZkCEyBAgAABAgSibqdkmii4PU+BJst11103G0P6N7/5TRx44IGRAg71ltSZ9PWvfz0bFuXjH/94vWwdlp6CAulp9jQWe5ooOXX0t7Sk+RNSJ3ea/yA93VpvItjKczRy/SvL2dHrqUP/Bz/4QTZ8UBpPvaW3TgYNGhSf+MQn4oUXXsieNG7LE9cdXd5GOd8Pf/jDrJO/3hsW6X5Kv410z6an09OQNWni7eZLCqgVCoXmybncXt2/wWR9/vnnR3qrqdaSnoZP93hXLp3xe0zDsD3zzDPZ3/n0t7X5MnDgwPj85z+fDSfWfCL25nltEyBAgAABAt1XoFfxH6H+Fdp920/JCRAgQIAAAQIdLrBw4cL4/e9/H3//+9/jH//4RxYgSJNfpk/qBG2tU77DC1RxwjTBcRqjPQ3vkZ7onTNnTmy66aZZ2VL50tPA9Tr9Kk7T4moj17/Fgq/izjRW+B/+8IfMNY2nnsbGT2OqpyFv0sSk6e0Ay78FVqxYkc1dMX369GxC1jSfxa677poFANoblPv3Wa0lgdX1G0xDjaW5M9JwY2nYptRmaTiddH+v6t+Njm651f17TL/vZDFlypRI93L6ne+zzz6R3tiwECBAgAABAj1bQECgZ7ev2hEgQIAAAQIECBAgQIAAAQIECBAgQIAAgUzAkEFuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgkAMBAYEcNLIqEiBAgAABAgQIECBAgAABAgQIECBAgAABAQH3AAECBAgQIECAAAECBAgQIECAAAECBAgQyIGAgEAOGlkVCRAgQIAAAQIECBAgQIAAAQIECBAgQICAgIB7gAABAgQIECBAgAABAgQIECBAgAABAgQI5EBAQCAHjayKBAgQIECAAAECBAgQIECAAAECBAgQIEBAQMA9QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEciAgIJCDRlZFAgQIECBAgAABAgQIECBAgAABAgQIECAgIOAeIECAAAECBAgQIECAAAECBAgQIECAAAECORAQEMhBI6siAQIECBAgQIAAAQIECBAgQIAAAQIECBAQEHAPECBAgAABAgQIECBAgAABAgQIECBAgACBHAgICOSgkVWRAAECBAgQIECAAAECBAgQIECAAAECBAgICLgHCBAgQIAAAQIECBAgQIAAAQIECBAgQIBADgQEBHLQyKpIgAABAgQIECBAgAABAgQIECBAgAABAgQEBNwDBAgQIECAAAECBAgQIECAAAECBAgQIEAgBwICAjloZFUkQIAAAQIECBAgQIAAAQIECBAgQIAAAQICAu4BAgQIECBAgAABAgQIECBAgAABAgQIECCQAwEBgRw0sioSIECAAAECBAgQIECAAAECBAgQIECAAAEBAfcAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIgYCAQA4aWRUJECBAgAABAgQIECBAgAABAgQIECBAgICAgHuAAAECBAgQIECAAAECBAgQIECAAAECBAjkQEBAIAeNrIoECBAgQIAAAQIECBAgQIAAAQIECBAgQEBAwD1AgAABAgQIECBAgAABAgQIECBAgAABAgRyICAgkINGVkUCBAgQIECAAAECBAgQIECAAAECBAgQICAg4B4gQIAAAQIECBAgQIAAAQIECBAgQIAAAQI5EBAQyEEjqyIBAgQIECBAgAABAgQIECBAgAABAgQIEBAQcA8QIECAAAECBAgQIECAAAECBAgQIECAAIEcCAgI5KCRVZEAAQIECBAgQIAAAQIECBAgQIAAAQIECAgIuAcIECBAgAABAgQIECBAgAABAgQIECBAgEAOBAQEctDIqkiAAAECBAgQIECAAAECBAgQIECAAAECBAQE3AMECBAgQIAAAQIECBAgQIAAAQIECBAgQCAHAgICOWhkVSRAgAABAgQIECBAgAABAgQIECBAgAABAgIC7gECBAgQIECAAAECBAgQIECAAAECBAgQIJADAQGBHDSyKhIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQEB9wABAgQIECBAgAABAgQIECBAgAABAgQIEMiBgIBADhpZFQkQIECAAAECBAgQIECAAAECBAgQIECAgICAe4AAAQIECBAgQIAAAQIECBAgQIAAAQIECORAQEAgB42sigQIECBAgAABAgQIECBAgAABAgQIECBAQEDAPUCAAAECBAgQIECAAAECBAgQIECAAAECBHIgICCQg0ZWRQIECBAgQIAAAQIECBAgQIAAAQIECBAgICDgHiBAgAABAgQIECBAgAABAgQIECBAgAABAjkQEBDIQSOrIgECBAgQIECAAAECBAgQIECAAAECBAgQEBBwDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRwICAjkoJFVkQABAgQIECBAgAABAgQIECBAgAABAgQIrImgMQUKhUJMnjw5pkyZEi+88EK89tprsWjRouyzePHi6N+/fwwcODAGDRoUG2ywQYwZMybGjRsXY8eOzfY1Zq2UigABAgQIECBAgAABAgQIECBAgAABAgS6SqBXseO50FUXd91qgXnz5sX1118fV199dcyYMaM6Qyspffr0iWOOOSbOOuusGDVqVCu57SZAgAABAgQIECBAgAABAgQIECBAgACBvAgICDRQS99www1xyimnxNtvv73KpVpjjTWyoMBFF10UvXr1WuXzOQEBAgQIECBAgAABAgQIECBAgAABAgQIdG8BAYEGab877rgjPv7xj8eKFSvKJRo+fHjsscceMWLEiBg6dGg2RFC/fv2ib9++sWzZsliyZEksXLgwZs+eHTNnzozHH388pk+fXj4+rRx99NFx4403Cgo0UbFBgAABAgQIECBAgAABAgQIECBAgACB/AkICDRAmz/44IPx4Q9/OJYuXZqV5sgjj4zzzjsvRo8e3e7SpcDApZdeGtdcc0352IkTJ8aECRPK21YIECBAgAABAgQIECBAgAABAgQIECBAIH8CAgIN0OZ77713PPTQQ1lJTj755Gz+gFUt1ne+850488wzs9NsueWW2XwEa65pDulVdV3V459//vnszY62nmfdddeNbbbZpq3Z5SNAgAABAgQIECBAgAABAgQIECBAgEBdAQGBujSds2POnDmxySabRJrbedy4cfHoo49GGv+/I5ZPfOIT8bOf/Sw71QsvvBDbbbddR5zWOd6lwHe/+9047bTT2n307bffHocddli7j3MAAQIECBAgQIAAAQIECBAgQIAAAQIEKgU6pue58ozW2yXw5z//OQsGpIMOOeSQDgsGpPONHz8+fWXLjBkzSqu+u0jg5ZdffldXfuWVV97VcQ4iQIAAAQIECBAgQIAAAQIECBAgQIBApYAxZCo1umA9TQpcWkaNGlVa7ZDvrbfeunyeN998s7xupWsF9ttvvzj11FNbLcTXv/71eO6551rNJwMBAgQIECBAgAABAgQIECBAgAABAgTaIuANgbYorcY8I0eOLJ/9qaeeKq93xMrDDz9cPs3OO+9cXrdCgAABAgQIECBAgAABAgQIECBAgAABAvkTEBDo4jYfNmxY9OvXLyvFLbfcEnPnzu2QEqU3D6699trsXH379o0ddtihQ87rJAQIECBAgAABAgQIECBAgAABAgQIECDQPQUEBLq43dIEwieddFJWildffTUOPvjgVQ4KLF68OA499NCYNm1adt4999wz+vTp08U1dXkCBAgQIECAAAECBAgQIECAAAECBAgQ6EoBAYGu1P+/a59zzjkxcODAbGvKlCkxYsSIOP300+O3v/1tpM79tiwrV66MqVOnxiWXXBJbbbVVTJ48OTts8ODB8ZOf/KQtp5CHAAECBAgQIECAAAECBAgQIECAAAECBHqwgEmFG6BxN9poo3jggQfikEMOiTlz5sSCBQvi8ssvzz69evWKzTffPIYPHx6DBg2KtddeO9IQQMuXL48lS5ZEGhpo9uzZMWvWrFi6dGmT2vTu3TsmTZqUHdtkhw0CBAgQIECAAAECBAgQIECAAAECBAgQyJ2AgECDNPluu+0Wjz32WJxyyilZcKBUrEKhEK+//nr2KaW15XvHHXeMq666Kvbaa6+2ZJeHAAECBAgQIECAAAECBAgQIECAAAECBHq4gCGDGqiB01A/v/71r+PJJ5+M4447LkaOHNmu0q233npx9NFHx1133RXPPPOMYEC79GQmQIAAAQIECBAgQIAAAQIECBAgQIBAzxbwhkADtu/YsWPj+uuvz0qWhhB6+umns6GE5s+fH2+99VY2TFC/fv1iwIAB2ScNJzR69OjYYostGrA2ikSAAAECBAgQIECAAAECBAgQIECAAAECjSAgINAIrdBCGdL8AgceeGALOewiQIAAAQIECBAgQIAAAQIECBAgQIAAAQKtCxgyqHUjOQgQIECAAAECBAgQIECAAAECBAgQIECAQLcXEBDo5k24fPnyeO211yJ9WwgQIECAAAECBAgQIECAAAECBAgQIECAQD0BAYF6Mg2c/txzz2WTB++4447Rv3//GDFiRKyzzjqx7bbbxn/8x3/EU0891cClVzQCBAgQIECAAAECBAgQIECAAAECBAgQ6AoBAYGuUG92ze233z769OmTff72t7812/vvzfQWwPnnnx+77rpr3HTTTZECA6U3A955553405/+FLfeemvstttuMWHChJg1a9a/D7ZGgAABAgQIECBAgAABAgQIECBAgAABArkWMKlwAzR/6sxPn7QUCoW6JUpP/995551195d2pHPcfPPN8fTTT8cTTzwRgwYNKu3yTYAAAQIECBAgQIAAAQIECBAgQIAAAQI5FRAQ6CYN/9Of/rRJMGD99dePL37xizFmzJjYZptt4s0334yXXnopfvWrX2VvCaRqpTcGjjnmmLjrrrtijTW8DNJNmloxCRAgQIAAAQIECBAgQIAAAQIECBAgsFoEBARWC2vHnnTu3LlZ53/prAcddFBcd911sfHGG5eSsu8999wzTjzxxHjwwQfjuOOOi5kzZ8Y999wTF198cZx99tlN8togQIAAAQIECBAgQIAAAQIECBAgQIAAgXwJeGy8G7T373//+5g3b15W0iFDhsTEiROrggGV1dh7773jtttuy+YkSOlp+CALAQIECBAgQIAAAQIECBAgQIAAAQIECORbwBsC3aD9p02bVi7lueeeG+utt155u97K2LFj49RTT43vfOc78eKLL8aiRYuif//+9bKvlvTnn38+pk6d2qHnfuONN7J5Fs4444wOPa+TESBAgAABAgQIECBAgAABAgQIECBAoKcLCAh0gxZOHeulZddddy2ttvr9wQ9+MAsIrFy5MuuY/8AHPtDqMR2VIU1svMcee8T8+fM76pRNzpOGS/rEJz7RJM0GAQIECBAgQIAAAQIECBAgQIAAAQIECNQXEBCob9MwezbaaKNyWYYPH15eb21l6623LmdJEwx3ZkCgV69eMWHChGw+g3IhOmAl1WPFihWx9tprd8DZnIIAAQIECBAgQIAAAQIECBAgQIAAAQL5ERAQ6AZtvf3225dL+fTTT8eHP/zh8nZLK6+88kp597Bhw8rrnbVyzTXXdPil0psBc+bMiQEDBnT4uZ2QAAECBAgQIECAAAECBAgQIECAAAECPVnApMLdoHUPPPDA8gTBkydPbnOJ08TCpWXUqFGlVd8ECBAgQIAAAQIECBAgQIAAAQIECBAgkEMBAYEGa/R99tknjj766LjsssvioYceigULFsSWW24Zn/zkJ7OS3njjjTF79uxWS33PPffEDTfckOVLQw4NGTKk1WNkIECAAAECBAgQIECAAAECBAgQIECAAIGeK2DIoAZr2xkzZkT63HTTTVnJ1lhjjUhzAWy22WbZ9uuvvx4HHHBAPPzwwzF48OCq0qf93//+9+Nb3/pWed8FF1wQaUx/CwECBAgQIECAAAECBAgQIECAAAECBAjkV8AbAg3Q9ocddli8973vjb59+1aVZuXKlTF9+vSoHCropZdeisrhgEoHHXzwwbHFFlvEpZdeGum4tOy8885x0kknlbL4JkCAAAECBAgQIECAAAECBAgQIECAAIGcCnhDoAEaPnXgp2X58uXx4osvxrRp05p8/vWvf1WVMg0j1HyZN29ek6SxY8fGrbfeGr17926SboMAAQIECBAgQIAAAQIECBAgQIAAAQIE8icgINBAbd6nT5/Yaaedss+xxx5bLtnMmTOzAMHUqVPLgYKRI0eW95dW0lwBaRkwYECccMIJ8c1vfrPmWwel/L4JECBAgAABAgQIECBAgAABAgQIECBAID8CAgLdoK2HDx8e6TN+/PgWS/uZz3wmzjjjjBg3blyk4IKFAAECBAgQIECAAAECBAgQIECAAAECBAiUBAQEShI94PsjH/lID6iFKhAgQIAAAQIECBAgQIAAAQIECBAgQIDA6hAwqfDqUHVOAgQIECBAgAABAgQIECBAgAABAgQIECDQYAICAg3WIIpDgAABAgQIECBAgAABAgQIECBAgAABAgRWh4Ahg1aH6iqcc9myZfHII4/EM888E88++2wMHTo03ve+98Xuu+8em266abvOfOihh8by5cuzY375y1+261iZCRAgQIAAAQIECBAgQIAAAQIECBAgQKBnCQgINFB7vvjii3H00UfH1KlTq0rVu3fvOP744+Oiiy6KjTfeuGp/rYRf//rXkQIMFgIECBAgQIAAAQIECBAgQIAAAQIECBAgYMigBrkH7r333th1111rBgNSEVesWBHXXntt7LLLLvHYY481SKkVgwABAgQIECBAgAABAgQIECBAgAABAgS6i4CAQAO01MKFC+Ozn/1sLF68OCtNv3794iMf+UiceeaZceSRR8Zmm21WLuVf//rX2G+//eK+++4rp1khQIAAAQIECBAgQIAAAQIECBAgQIAAAQKtCRgyqDWhTth/wQUXxOzZs7Mr7bTTTjFp0qTYbrvtyldOw/5873vfiwsvvDDmz58fS5cujcMPPzxuv/32OOigg8r5rBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXoC3hCoJ9OJ6TfffHN2tbXXXjvuvPPOJsGAtGOttdaKL3/5y/HEE0/EsGHDsrwpKHDEEUcYPijT8D8ECBAgQIAAAQIECBAgQIAAAQIECBAg0JqAgEBrQqt5/6JFi8pvB3zqU5+KESNG1L3itttumwUARo8eneVZsmRJfPSjH40//elPdY+xgwABAgQIECBAgAABAgQIECBAgAABAgQIJAEBgS6+D6ZPn14uwZgxY8rr9VaGDBkSkydPjq222irLMnfu3Gy+gTfeeKPeIdIJECBAgAABAgQIECBAgAABAgQIECBAgICAQFffAwsWLCgXYYMNNiivt7QyePDguP/++yN9p+XVV1/N3hQoTUrc0rH2ESBAgAABAgQIECBAgAABAgQIECBAgEA+Bbwh0MXtXnrSPxXjpZdeanNp0nF33313pHkH0jJlypQ49thjo1AotPkcMhIgQIAAAQIECBAgQIAAAQIECBAgQIBAfgQEBLq4rTfffPNYZ511slLcc8897erQHzduXNx0002xxhr/24y33XZbfOlLX+riGrk8AQIECBAgQIAAAQIECBAgQIAAAQIECDSigIBAF7dKr169Yvfdd89K8eijj8Y111zTrhJ97GMfi8suu6x8zBVXXBFf/OIX45133imnWSFAgAABAgQIECBAgAABAgQIECBAgAABAgICDXAPXHzxxeVSfO5zn4sTTjghXnnllTa/LZACAJVvBqSgwP777x8rV64sn9cKAQIECBAgQIAAAQIECBAgQIAAAQIECORbQECgAdo/vSFw1FFHlUty3XXXRZojYMCAAZHeGmjLkt4S+MIXvlDO+tBDD3lLoKxhhQABAgQIECBAgAABAgQIECBAgAABAgQEBBrkHkhBgPR2QOXy9ttvx8CBAyuT6q6noYfSmwHnn39+9OnTp24+OwgQIECAAAECBAgQIECAAAECBAgQIEAgnwICAg3S7n379o0rr7wy7rzzzvjQhz5Unmg4TTrcnuXcc8+NadOmxT777NOew+QlQIAAAQIECBAgQIAAAQIECBAgQIAAgR4uICDQYA08fvz4eOCBB2LevHnZcEEbbrhhu0u4/fbbx+9+97uYOHFiHHbYYbHNNtu0+xwOIECAAAECBAgQIECAAAECBAgQIECAAIGeJbBmz6pOz6lNGvZnjz32WKUKTZgwIdLHQoAAAQIECBAgQIAAAQIECBAgQIAAAQIEvCHgHiBAgAABAgQIECBAgAABAgQIECBAgAABAjkQEBDIQSOrIgECBAgQIECAAAECBAgQIECAAAECBAgQEBBwDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRwICAjkoJFVkQABAgQIECBAgAABAgQIECBAgAABAgQICAi4BwgQIECAAAECBAgQIECAAAECBAgQIECAQA4EBARy0MiqSIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBATcAwQIECBAgAABAgQIECBAgAABAgQIECBAIAcCAgI5aGRVJECAAAECBAgQIECAAAECBAgQIECAAAECAgLuAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAMBAYEcNLIqEiBAgAABAgQIECBAgAABAgQIECBAgAABAQH3AAECBAgQIECAAAECBAgQIECAAAECBAgQyIGAgEAOGlkVCRAgQIAAAQIECBAgQIAAAQIECBAgQICAgIB7gAABAgQIECBAgAABAgQIECBAgAABAgQI5EBAQCAHjayKBAgQIECAAAECBAgQIECAAAECBAgQIEBAQMA9QIAAAQIECBAgQIAAAQIECBAgQIAAAQIEciAgIJCDRlZFAgQIECBAgAABAgQIECBAgAABAgQIECAgIOAeIECAAAECBAgQIECAAAECBAgQIECAAAECORAQEMhBI6siAQIECBAgQIAAAQIECBAgQIAAAQIECBAQEHAPECBAgAABAgQIECBAgAABAgQIECBAgACBHAgICOSgkVWRAAECBAgQIECAAAECBAgQIECAAAECBAgICLgHCBAgQIAAAQIECBAgQIAAAQIECBAgQIBADgQEBHLQyKpIgAABAgQIECBAgAABAgQIECBAgAABAgQEBNwDBAgQIECAAAECBAgQIECAAAECBAgQIEAgBwICAjloZFUkQIAAAQIECBAgQIAAAQIECBAgQIAAAQICAu4BAgQIECBAgAABAgQIECBAgAABAgQIECCQAwEBgRw0sioSIECAAAECBAgQIECAAAECBAgQIECAAAEBAfcAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIgYCAQA4aWRUJECBAgAABAgQIECBAgAABAgQIECBAgICAgHuAAAECBAgQIECAAAECBAgQIECAAAECBAjkQEBAIAeNrIoECBAgQIAAAQIECBAgQIAAAQIECBAgQEBAwD1AgAABAgQIECBAgAABAgQIECBAgAABAgRyICAgkINGVkUCBAgQIECAAAECBAgQIECAAAECBAgQICAg4B4gQIAAAQIECBAgQIAAAQIECBAgQIAAAQI5EBAQyEEjqyIBAgQIECBAgAABAgQIECBAgAABAgQIEBAQcA8QIECAAAECBAgQIECAAAECBAgQIECAAIEcCAgI5KCRVZEAAQIECBAgQIAAAQIECBAgQIAAAQIECAgIuAcIECBAgAABAgQIECBAgAABAgQIECBAgEAOBAQEctDIqkiAAAECBAgQIECAAAECBAgQIECAAAECBAQE3AMECBAgQIAAAQIECBAgQIAAAQIECBAgQCAHAgICOWhkVSRAgAABAgQIECBAgAABAgQIECBAgAABAgIC7gECBAgQIECAAAECBAgQIECAAAECBAgQIJADAQGBHDSyKhIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQEB9wABAgQIECBAgAABAgQIECBAgAABAgQIEMiBgIBADhpZFQkQIECAAAECBAgQIECAAAECBAgQIECAgICAe4AAAQIECBAgQIAAAQIECBAgQIAAAQIECORAQEAgB42sigQIECBAgAABAgQIECBAgAABAgQIECBAQEDAPUCAAAECBAgQIECAAAECBAgQIECAAAECBHIgICCQg0ZWRQIECBAgQIAAAQIECBAgQIAAAQIECBAgICDgHiBAgAABAgQIECBAgAABAgQIECBAgAABAjkQEBDIQSOrIgECBAgQIECAAAECBAgQIECAAAECBAgQEBBwDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRwICAjkoJFVkQABAgQIECBAgAABAgQIECBAgAABAgQICAi4BwgQIECAAAECBAgQIECAAAECBAgQIECAQA4EBARy0MiqSIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBATcAwQIECBAgAABAgQIECBAgAABAgQIECBAIAcCAgI5aGRVJECAAAECBAgQIECAAAECBAgQIECAAAECAgLuAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAMBAYEcNLIqEiBAgAABAgQIECBAgAABAgQIECBAgACBNRE0pkChUIjJkyfHlClT4oUXXojXXnstFi1alH0WL14c/fv3j4EDB8agQYNigw02iDFjxsS4ceNi7Nix2b7GrJVSESBAgAABAgQIECBAgAABAgQIECBAgEBXCQgIdJV8nevOmzcvrr/++rj66qtjxowZdXJVJ0+aNClL7NOnTxxzzDFx1llnxahRo6ozSiFAgAABAgQIECBAgAABAgQIECBAgACBXAoYMqiBmv2GG26IIUOGxGmnndauYEBlFZYvXx7XXXddjB49Or72ta9FetPAQoAAAQIECBAgQIAAAQIECBAgQIAAAQIEvCHQIPfAHXfcESeeeGKsWLGiXKLhw4fHHnvsESNGjIihQ4dmQwT169cv+vbtG8uWLYslS5bEwoULY/bs2TFz5sx4/PHHY/r06dnxK1eujEsuuSRmzZoVN954Y/Tq1at8XisECBAgQIAAAQIECBAgQIAAAQIECBAgkD8BAYEGaPMHH3wwjjrqqHIw4Mgjj4zzzjsve8q/vcVLgYFLL700rrnmmuzQiRMnxkEHHRQTJkxo76nkJ0CAAAECBAgQIECAAAECBAgQIECAAIEeJGDIoAZozHPPPTeWLl2aleTkk0+OW2+99V0FA9IJ0lsFaf6Bb3/72+WanX322fHOO++Ut60QIECAAAECBAgQIECAAAECBAgQIECAQP4EBAS6uM3nzJkTDz/8cJ5tEW4AAEAASURBVFaKcePGxZVXXtkhJTrjjDOyyYXTyV577bV3PSdBhxTGSQgQIECAAAECBAgQIECAAAECBAgQIECgywUEBLq4Cf785z+XJ/495JBDYo01Oq5Jxo8fX67djBkzyutWCBAgQIAAAQIECBAgQIAAAQIECBAgQCB/Ah3X+5w/uw6pcZoUuLSMGjWqtNoh31tvvXX5PG+++WZ53QoBAgQIECBAgAABAgQIECBAgAABAgQI5E9AQKCL23zkyJHlEjz11FPl9Y5YKQ1FlM618847d8QpnYMAAQIECBAgQIAAAQIECBAgQIAAAQIEuqmAgEAXN9ywYcOiX79+WSluueWWmDt3boeUKL15cO2112bn6tu3b+ywww4dcl4nIUCAAAECBAgQIECAAAECBAgQIECAAIHuKSAg0MXtluYMOOmkk7JSvPrqq3HwwQevclBg8eLFceihh8a0adOy8+65557Rp0+fLq6pyxMgQIAAAQIECBAgQIAAAQIECBAgQIBAVwoICHSl/v9d+5xzzomBAwdmW1OmTIkRI0bE6aefHr/97W8jde63ZVm5cmVMnTo1Lrnkkthqq61i8uTJ2WGDBw+On/zkJ205hTwECBAgQIAAAQIECBAgQIAAAQIECBAg0IMF1uzBdes2Vdtoo43igQceiEMOOSTmzJkTCxYsiMsvvzz79OrVKzbffPMYPnx4DBo0KNZee+1IQwAtX748lixZEmlooNmzZ8esWbNi6dKlTercu3fvmDRpUnZskx02CBAgQIAAAQIECBAgQIAAAQIECBAgQCB3AgICDdLku+22Wzz22GNxyimnZMGBUrEKhUK8/vrr2aeU1pbvHXfcMa666qrYa6+92pJdHgIECBAgQIAAAQIECBAgQIAAAQIECBDo4QKGDGqgBk5D/fz617+OJ598Mo477rgYOXJku0q33nrrxdFHHx133XVXPPPMM4IB7dKTmQABAgQIECBAgAABAgQIECBAgAABAj1bwBsCDdi+Y8eOjeuvvz4rWRpC6Omnn86GEpo/f3689dZb2TBB/fr1iwEDBmSfNJzQ6NGjY4sttmjA2igSAQIECBAgQIAAAQIECBAgQIAAAQIECDSCgIBAI7RCC2VI8wsceOCBLeSwiwABAgQIECBAgAABAgQIECBAgAABAgQItC5gyKDWjeQgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLdXkBAoNs3oQoQIECAAAECBAgQIECAAAECBAgQIECAAIHWBQwZ1LpRl+QoFAoxefLkmDJlSrzwwgvx2muvxaJFi7LP4sWLo3///jFw4MAYNGhQbLDBBjFmzJgYN25cpPkH0j4LAQIECBAgQIAAAQIECBAgQIAAAQIECBCoFBAQqNRogPV58+ZlEwpfffXVMWPGjDaXaNKkSVnePn36xDHHHBNnnXVWjBo1qs3Hy0iAAAECBAgQIECAAAECBAgQIECAAAECPVvAkEEN1L433HBDDBkyJE477bR2BQMqq7B8+fK47rrrYvTo0fG1r30t0psGFgIECBAgQIAAAQIECBAgQIAAAQIECBAg4A2BBrkH7rjjjjjxxBNjxYoV5RINHz489thjjxgxYkQMHTo0GyKoX79+0bdv31i2bFksWbIkFi5cGLNnz46ZM2fG448/HtOnT8+OX7lyZVxyySUxa9asuPHGG6NXr17l81ohQIAAAQIECBAgQIAAAQIECBAgQIAAgfwJCAg0QJs/+OCDcdRRR5WDAUceeWScd9552VP+7S1eCgxceumlcc0112SHTpw4MQ466KCYMGFCe08lPwECBAgQIECAAAECBAgQIECAAAECBAj0IAFDBjVAY5577rmxdOnSrCQnn3xy3Hrrre8qGJBOkN4qSPMPfPvb3y7X7Oyzz4533nmnvG2FAAECBAgQIECAAAECBAgQIECAAAECBPIn4A2BLm7zOXPmxMMPP5yVYty4cXHllVd2SInOOOOMmDZtWvzsZz+L1157LZuTYLvttuuQc7f1JOnthEceeaSt2duUb8GCBVm+yqGV2nRgN8y0ePHirNS/+MUv4pVXXmlTDTbffPNs7og11/TTbhOYTAQIECBAgAABAgQIECBAgAABAgRyJKDXsIsb+89//nN54t9DDjkk1lij417aGD9+fBYQSFWcMWNGdGZAIE1m/MlPfjLSJMerY3nppZfi4IMPXh2nbphz/uMf/8jKkuaGSJ+2Lmneif3337+t2eUjQIAAAQIECBAgQIAAAQIECBAgQCAnAgICXdzQaVLg0jJq1KjSaod8b7311uXzvPnmm+X1zlhJkxj/6Ec/6vA3BNIbD2ky5Y626gyT9l4jBVXSktpxyy23zNZb+p80F0WabLo0/FRLee0jQIAAAQIECBAgQIAAAQIECBAgQCB/AgICXdzmI0eOLJfgqaeeiiOOOKK8vaorpaGI0nl23nnnVT1du48/7rjjIn06crnrrruygEDv3r078rQNfa70xP9hhx3WahmfeOKJLCDQakYZCBAgQIAAAQIECBAgQIAAAQIECBDIpUDHjU+TS75Vr/SwYcOiX79+2YluueWWmDt37qqftHiG9ObBtddem52rb9++scMOO3TIeZ2EAAECBAgQIECAAAECBAgQIECAAAECBLqngIBAF7dbmjPgpJNOykrx6quvZuPir2pQIE1Ge+ihh2aTCqcT77nnntGnT58urqnLEyBAgAABAgQIECBAgAABAgQIECBAgEBXCggIdKX+/137nHPOiYEDB2ZbU6ZMiREjRsTpp58ev/3tbyN17rdlWblyZUydOjUuueSS2GqrrWLy5MnZYYMHD46f/OQnbTmFPAQIECBAgAABAgQIECBAgAABAgQIECDQgwXMIdAAjbvRRhvFAw88EIccckjMmTMnFixYEJdffnn2SZPzbr755jF8+PAYNGhQrL322pGGAFq+fHk2ln4aGmj27Nkxa9asqslk0zj7kyZNyo5tgGoqAgECBAgQIECAAAECBAgQIECAAAECBAh0oYCAQBfiV156t912i8ceeyxOOeWULDhQ2lcoFOL111/PPqW0tnzvuOOOcdVVV8Vee+3VluzyECBAgAABAgQIECBAgAABAgQIECBAgEAPFzBkUAM1cBrq59e//nU8+eSTcdxxx8XIkSPbVbr11lsvjj766LjrrrvimWeeEQxol57MBAgQIECAAAECBAgQIECAAAECBAgQ6NkC3hBowPYdO3ZsXH/99VnJ0hBCTz/9dDaU0Pz58+Ott96KNExQv379YsCAAdknDSc0evTo2GKLLRqwNopEgAABAgQIECBAgAABAgQIECBAgAABAo0gICDQCK3QQhnS/AIHHnhgCznsIkCAAAECBAgQIECAAAECBAgQIECAAAECrQsYMqh1IzkIECBAgAABAgQIECBAgAABAgQIECBAgEC3F/CGQIM14bJly+KRRx7J5gB49tlnY+jQofG+970vdt9999h0003bVdpDDz00li9fnh3zy1/+sl3HykyAAAECBAgQIECAAAECBAgQIECAAAECPUtAQKCB2vPFF1/MJgWeOnVqVal69+4dxx9/fFx00UWx8cYbV+2vlZAmKE4BBgsBAgQIECBAgAABAgQIECBAgAABAgQIEDBkUIPcA/fee2/suuuuUSsYkIq4YsWKuPbaa2OXXXaJxx57rEFKrRgECBAgQIAAAQIECBAgQIAAAQIECBAg0F0EBAQaoKUWLlwYn/3sZ2Px4sVZafr16xcf+chH4swzz4wjjzwyNttss3Ip//rXv8Z+++0X9913XznNCgECBAgQIECAAAECBAgQIECAAAECBAgQaE3AkEGtCXXC/gsuuCBmz56dXWmnnXaKSZMmxXbbbVe+chr253vf+15ceOGFMX/+/Fi6dGkcfvjhcfvtt8dBBx1UzmeFAAECBAgQIECAAAECBAgQIECAAAECBAjUE/CGQD2ZTky/+eabs6utvfbaceeddzYJBqQda621Vnz5y1+OJ554IoYNG5blTUGBI444wvBBmYb/IUCAAAECBAgQIECAAAECBAgQIECAAIHWBAQEWhNazfsXLVpUfjvgU5/6VIwYMaLuFbfddtssADB69Ogsz5IlS+KjH/1o/OlPf6p7jB0ECBAgQIAAAQIECBAgQIAAAQIECBAgQCAJCAh08X0wffr0cgnGjBlTXq+3MmTIkJg8eXJstdVWWZa5c+dm8w288cYb9Q6RToAAAQIECBAgQIAAAQIECBAgQIAAAQIEBAS6+h5YsGBBuQgbbLBBeb2llcGDB8f9998f6Tstr776avamQGlS4paOtY8AAQIECBAgQIAAAQIECBAgQIAAAQIE8ingDYEubvfSk/6pGC+99FKbS5OOu/vuuyPNO5CWKVOmxLHHHhuFQqHN55CRAAECBAgQIECAAAECBAgQIECAAAECBPIjICDQxW29+eabxzrrrJOV4p577mlXh/64cePipptuijXW+N9mvO222+JLX/pSF9fI5QkQIECAAAECBAgQIECAAAECBAgQIECgEQUEBLq4VXr16hW77757VopHH300rrnmmnaV6GMf+1hcdtll5WOuuOKK+OIXvxjvvPNOOc0KAQIECBAgQIAAAQIECBAgQIAAAQIECBAQEGiAe+Diiy8ul+Jzn/tcnHDCCfHKK6+0+W2BFACofDMgBQX233//WLlyZfm8VggQIECAAAECBAgQIECAAAECBAgQIEAg3wICAg3Q/ukNgaOOOqpckuuuuy7SHAEDBgyI9NZAW5b0lsAXvvCFctaHHnrIWwJlDSsECBAgQIAAAQIECBAgQIAAAQIECBAgICDQIPdACgKktwMql7fffjsGDhxYmVR3PQ09lN4MOP/886NPnz5189lBgAABAgQIECBAgAABAgQIECBAgAABAvkUEBBokHbv27dvXHnllXHnnXfGhz70ofJEw2nS4fYs5557bkybNi322Wef9hwmLwECBAgQIECAAAECBAgQIECAAAECBAj0cAEBgQZr4PHjx8cDDzwQ8+bNy4YL2nDDDdtdwu233z5+97vfxcSJE+Owww6LbbbZpt3ncAABAgQIECBAgAABAgQIECBAgAABAgQI9CyBNXtWdXpObdKwP3vssccqVWjChAmRPhYCBAgQIECAAAECBAgQIECAAAECBAgQIOANAfcAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIgYCAQA4aWRUJECBAgAABAgQIECBAgAABAgQIECBAgICAgHuAAAECBAgQIECAAAECBAgQIECAAAECBAjkQEBAIAeNrIoECBAgQIAAAQIECBAgQIAAAQIECBAgQEBAwD1AgAABAgQIECBAgAABAgQIECBAgAABAgRyICAgkINGVkUCBAgQIECAAAECBAgQIECAAAECBAgQICAg4B4gQIAAAQIECBAgQIAAAQIECBAgQIAAAQI5EBAQyEEjqyIBAgQIECBAgAABAgQIECBAgAABAgQIEBAQcA8QIECAAAECBAgQIECAAAECBAgQIECAAIEcCAgI5KCRVZEAAQIECBAgQIAAAQIECBAgQIAAAQIECAgIuAcIECBAgAABAgQIECBAgAABAgQIECBAgEAOBAQEctDIqkiAAAECBAgQIECAAAECBAgQIECAAAECBAQE3AMECBAgQIAAAQIECBAgQIAAAQIECBAgQCAHAgICOWhkVSRAgAABAgQIECBAgAABAgQIECBAgAABAgIC7gECBAgQIECAAAECBAgQIECAAAECBAgQIJADAQGBHDSyKhIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQEB9wABAgQIECBAgAABAgQIECBAgAABAgQIEMiBgIBADhpZFQkQIECAAAECBAgQIECAAAECBAgQIECAgICAe4AAAQIECBAgQIAAAQIECBAgQIAAAQIECORAQEAgB42sigQIECBAgAABAgQIECBAgAABAgQIECBAQEDAPUCAAAECBAgQIECAAAECBAgQIECAAAECBHIgICCQg0ZWRQIECBAgQIAAAQIECBAgQIAAAQIECBAgICDgHiBAgAABAgQIECBAgAABAgQIECBAgAABAjkQEBDIQSOrIgECBAgQIECAAAECBAgQIECAAAECBAgQEBBwDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRwICAjkoJFVkQABAgQIECBAgAABAgQIECBAgAABAgQICAi4BwgQIECAAAECBAgQIECAAAECBAgQIECAQA4EBARy0MiqSIAAAQIECBAgQIAAAQIECBAgQIAAAQIEBATcAwQIECBAgAABAgQIECBAgAABAgQIECBAIAcCAgI5aGRVJECAAAECBAgQIECAAAECBAgQIECAAAECAgLuAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAMBAYEcNLIqEiBAgAABAgQIECBAgAABAgQIECBAgAABAQH3AAECBAgQIECAAAECBAgQIECAAAECBAgQyIGAgEAOGlkVCRAgQIAAAQIECBAgQIAAAQIECBAgQIDAmggIEOgZAkuXLs0qctZZZ8Ull1zSpkrttNNO8f3vfz969erVpvwyESBAgAABAgQIECBAgAABAgQIECDQfQUEBLpv2yk5gSYCpYDA888/3yS9pY2HH344vvrVr8YWW2zRUjb7CBAgQIAAAQIECBAgQIAAAQIECBDoAQICAj2gEVWBQKXAAQccEEOHDq1MqlpPwYOJEydm6StWrKjaL4EAAQIECBAgQIAAAQIECBAgQIAAgZ4nICDQ89pUjXIusPvuu8fYsWNbVJg/f345INBiRjsJECBAgAABAgQIECBAgAABAgQIEOgxAiYV7jFNqSIECBAgQIAAAQIECBAgQIAAAQIECBAgQKC+gIBAfRt7CBAgQIAAAQIECBAgQIAAAQIECBAgQIBAjxEQEOgxTakiBAgQIECAAAECBAgQIECAAAECBAgQIECgvoCAQH0bewgQIECAAAECBAgQIECAAAECBAgQIECAQI8REBDoMU2pIgQIECBAgAABAgQIECBAgAABAgQIECBAoL6AgEB9G3sIECBAgAABAgQIECBAgAABAgQIECBAgECPERAQ6DFNqSIECBAgQIAAAQIECBAgQIAAAQIECBAgQKC+gIBAfRt7CBAgQIAAAQIECBAgQIAAAQIECBAgQIBAjxEQEOgxTakiBAgQIECAAAECBAgQIECAAAECBAgQIECgvoCAQH0bewgQIECAAAECBAgQIECAAAECBAgQIECAQI8REBDoMU2pIgQIECBAgAABAgQIECBAgAABAgQIECBAoL6AgEB9G3sIECBAgAABAgQIECBAgAABAgQIECBAgECPEVizx9RERQgQeFcCF154YQwcOLBNxx5wwAFx4IEHtimvTAQIECBAgAABAgQIECBAgAABAgQINJZAhwYE3nrrrVhjjTVi3XXXbaxaKg0BAk0ElixZUt7+8Y9/XF5vbeWGG26If/7zn61ls58AAQIECBAgQIAAAQIECBAgQIAAgQYU6NCAwNSpU+Pggw+OI444Io4//vjYd999o1evXg1YbUUikG+B5cuXlwF222236NevX3m71sq8efPij3/8YyxevLjW7oZPmz9/fjz77LNRKBTaXNYRI0bEkCFD2pxfRgIECBAgQIAAAQIECBAgQIAAAQKNLtChAYFU2bfffjtuvPHG7DNs2LA49thj47jjjoutttqq0S2Uj0AuBT71qU/Fxhtv3GLdn3/++Swg0GKmBt75//7f/4spU6a0q4QDBgyI119/vc3DKbXr5DITIECAAAECBAgQIECAAAECBAgQ6AKBDp1UeJ111onevXuXq/GXv/wl0vjkW2+9dey1115x7bXXRnpS10KAAIHOFJg5c2Z2uTXXXDPWWmutFj99+vTJ8i5cuDDmzp3bmcV0LQIECBAgQIAAAQIECBAgQIAAAQKrVaBD3xBIQ4/Mnj07brrppuwNgWnTppUL/8gjj0T6nHrqqXHYYYdlQwrtt99+2ZwD5UxWCBAgsBoFvvrVr8bYsWNbvEIKBBxzzDEt5rGTAAECBAgQIECAAAECBAgQIECAQHcU6NA3BBLApptuGqeffnqk+QTSmONnnnlmbLbZZmWbNKTQxIkT44ADDogtt9wyzj777JgxY0Z5vxUCBAgQIECAAAECBAgQIECAAAECBAgQIECg4wU6PCBQWcQdd9wxvvWtb8WsWbPil7/8ZUyYMCHSsEKlJaVfdNFFsc0228See+4ZP/rRj+Ktt94q7fZNgAABAgQIECBAgAABAgQIECBAgAABAgQIdJDAag0IlMqY5hX48Ic/nL0Z8Pe//z1+8pOfxP777x9pPO/S8thjj8WnP/3p7G2CFDh44IEHolAolHb7JkCAAAECBAgQIECAAAECBAgQIECAAAECBFZB4N898qtwkvYcuu6668YnP/nJ7DNv3ry4//7746677sq+04TDixcvjptvvjn7pCGFTjjhhPjMZz4TG2+8cXsu0+3zpmDI5MmTY8qUKfHCCy/Ea6+9FosWLco+yah///4xcODAGDRoUGywwQYxZsyYGDduXDY+etpnIUCAAAECBAgQIECAAAECBAgQIECAAAEClQKdHhCovPg777yTBQDSvALLli2r3JWtp07wc889NxtW6OSTT45vfOMbWUd4VcYelJCCJNdff31cffXV7ZpbYdKkSZlCnz59sglRzzrrrBg1alQPklEVAgQIECBAgAABAgQIECBAgAABAgQIEFgVgU4ZMqiygG+++Wb88Ic/jL333js22WSTOPHEE+Puu++OJUuWZNnS8EIHHXRQHH744bHWWmtlaWnf5ZdfHrvssku8/vrrlafrUes33HBDDBkyJE477bR2BQMqEZYvXx7XXXddjB49Or72ta8ZdqkSxzoBAgQIECBAgAABAgQIECBAgAABAgRyLNApbwgsXbo07rnnnvjZz34W9913X823AdLT7Mcff3wce+yx2TwCqU3mzp2bPS1/4YUXRnpyfsaMGVmeNL9AT1vuuOOOLDiyYsWKctWGDx8ee+yxR4wYMSKGDh2aDRHUr1+/6Nu3b2aYAiULFy6M2bNnx8yZM+Pxxx+P6dOnZ8evXLkyLrnkkmxC5xtvvDF69epVPq8VAgQIECBAgAABAgQIECBAgAABAgQIEMifwGoLCKQx8B988MFsIuFbb7013nrrrSrdNAb+f/7nf2bzCbz//e+v2r/hhhvGGWecEXvuuWd88IMfjPT0expXP801kI7tKUtyOuqoo6IUDDjyyCPjvPPOy57yb28dU2Dg0ksvjWuuuSY7dOLEidkbF2miZgsBAgQIECBAgAABAgQIECBAgAABAgQI5Fegw4cMeu6557KhatLT7fvuu2/8+Mf/n707gY+iSBs//oQQ7kskqJwSCYLcp4Ci4sm5uIAoh7IoLgIqiwKK/BHQVQFBXAU8VpFTAVkVFlQQlFuuIPcpl+HScIcjEAj/96n37THRMN0z9CRz/OrzGaZnuqq66tthMumnq+qjDMEAvVP97rvvFr1r/fDhw/Lhhx9KZsGA9KdEF8vV6YI06boDP/zwQ/rdIb+t6yToKApNulaCBlB0yh9/krrr+gMjRozwFB8wYIBx87zBBgIIIIAAAggggAACCCCAAAIIIIAAAggggEDECbg6QmDJkiXmTv7MFHXam86dO5spf/Sita+pfPnysmrVKlNMp80Jl5SUlCTqpkkDH6NHj3alazqyYt26dWaaJl2cWadbqlSpkit1UwkCCCCAAAIIIIAAAggggAACCCCAAAIIIIBA6Am4OkLAmvLGYsiXL59ZE0Dv6N+1a5cMGjRI/AkGaH16UTtnzpxSpkwZz2gB6zih/Lxnzx7Pwr8tWrSQHDncOyWtWrXy0GhAgIQAAggggAACCCCAAAIIIIAAAggggAACCCAQuQKujhCwGHXO/y5duki7du2kYMGC1ttX9fzFF19IsWLFJDo6+qrqCbbCuiiwlXRhZTdTfHy8p7pjx455ttlAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiDwBVwMCekF7+/btUqFCBUeSepE6JibGUdDguuuuc1RnqGWKi4vzNHnNmjXSpk0bz+ur3bCmItJ6qlevfrXVUR4BBK4goIuoX7hw4Qp7//y2jgTSzz4SAggggAACCCCAAAIIIIAAAggggAACWSng3vw0/9Pq66+/3hMMOHHihLz//vvSqFEj2bhxY6Z90rv+Y2NjRafK0YV0IzHpFEjWmgjTp0+Xo0ePusKgIw8+/vhjU1fu3LmlSpUqrtRLJQggkFFA1wEpWbKk+X+s/5edPHLlyiV9+vTJWBGvEEAAAQQQQAABBBBAAAEEEEAAAQQQCLCAqwEBq60aANA70rt37y5Lly6VrVu3WrsyPOv8+efPn5c5c+aY6YW6du1qXmfIFOYv9E7hJ5980vRy9+7d0rx586sOCpw7d05atmxpFhXWinUKJ+5GDvMfJLqXbQI6KurQoUM+H3/hwoU+l6EAAggggAACCCCAAAIIIIAAAggggAACVyPg6pRB2pANGzbI7bffLsnJyZ52bdu2zbOdfiMlJSX9S3NH+759++S7777L8H64vxg4cKBMmDBBTp06JStXrpRy5cqJBkc0ONCwYUPJmzevLUFaWpqsX79e5s6dK++++64cPHjQlNF1F8aNG2dbngwIIHB1Arro+VtvvWVbybJly2TatGm2+ciAAAIIIIAAAggggAACCCCAAAIIIICA2wKuBwRefPFFTzCgRIkS0r9/f2nfvn2m7R45cqT07dtXJk2aJC+//LJogGD+/PkydepUeeSRRzItE45v6rRJGgTRqZN0+hENpowaNco8oqKiRB3Lli0rhQsXNsEBnQIoNTXVeOnUQPv375fExMQ/ja7QBZjVUsuSEEAgsAL6f1WnALNLVxoxZVeO/QgggAACCCCAAAIIIIAAAggggAACCFytgKsBAV0U95tvvjFtqlSpknz//fdmXQFvjdR1BzQocPfdd0uDBg3Mhe5+/frJww8/LHqBLVJSvXr1ZPny5dKjR48MIyR0sdIDBw6Yhy8WVatWlTFjxpg1HHwpR14EEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB8BRwdQ2BVatWeZTeeecd22CAJ/P/bNSuXVt69epl3tK73fURaal8+fIyb948Wb16tXTu3Fni4uJ8IihSpIh07NhRZs6cKWvXriUY4JMemRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfAWcHWEwK5du4xWrly5pHHjxj7L3XfffTJixAhTTqfVcDL9hs8HCYECderUkfHjx5uW6hRCCQkJZiohXWPg5MmTotME5cmTRwoUKGAeOiVQ5cqVpVSpUiHQO5qIAAIIIIAAAggggAACCCCAAAIIIIAAAgggkB0CrgYEjhw5YvqgF6tz5PB98MF1113nMdAL4SQRXV+gSZMmUCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggcFUCvl+193I46w51vZN99+7dXnJmvmvjxo2eHboGAQkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTcEXA1IFChQgVPqz7//HPPttONWbNmmazR0dFmChyn5SI5X2pqquzdu9csxhzJDvQdAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwLuAqwGBBx98UPLly2eO+PLLL0v6RYa9N0Nk3LhxYgURqlSpYubItysTqfs3bdpkFg+uWrWq5M+fX8qVK2fcb775ZmnXrp2sWbMmUmnoNwIIIIAAAggggAACCCCAAAIIIIAAAggggMAVBFwNCBQuXFg6depkDqV3rt99993ywgsviLW2QGZt2Llzp3Tp0kW6detmdkdFRcmoUaMyyxq2791yyy0SExNjHocOHbpiP9V0yJAhUrt2bfn0009FAwP6nqaLFy/Kjh07TFClXr160qFDB0lMTLxiXexAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiCwBVxcVVroRI0bI4sWLZdu2bXLmzBkZPny4vPPOO3LTTTdJmTJlpHTp0nL+/HnZv3+/uWC9a9cuuXTpkke9d+/e0rhxY8/rSNjQi/n60HT58uUrdlnv/v/qq6+uuN/aoXV89tlnkpCQYEZpaKCGhAACCCCAAAIIIIAAAggggAACCCCAAAIIIBDZAq4HBAoWLCi6FkDbtm1lw4YNRjclJUU2b95sHlfizpEjhxkp8Prrr18pS0S/P3HixAzBgGuuuUZ69eolNWrUEF274dixYyYIM3fuXM/USzpiQEdszJw5U9SXhEBWCiQlJYkuMO405cqVywQMneYnHwIIIIAAAggggAACCCCAAAIIIIAAAgj4JuB6QEAPHx8fb+5OHzlypHzwwQeyZ88er6264447zDRBtWrV8povUncePXrUXPy3+t+sWTP55JNPpHjx4tZb5vm2226TJ554QhYtWiSdO3eWffv2yezZs+WNN96QAQMGZMjLCwQCKaCjhHSkT1pamk+H0RFGzz//vE9lyIwAAggggAACCCCAAAIIIIAAAggggAACzgQCEhDQQ+fMmdOsH6BrCOhc98uWLZPDhw/Lb7/9ZhbALV++vFiPsmXLOmtthOb68ccf5cSJE6b3JUuWlClTpkiRIkWuqHHnnXfKjBkzpGHDhmaNAZ0+iIDAFbnYEQCBLVu2+BwM0GZs3LgxAK2hSgQQQAABBBBAAAEEEEAAAQQQQAABBBBQgYAFBNLzVqlSRfRB8k9g3bp1noIvv/yy12CAlbFOnTry7LPPio7S2Lp1q1nPIX/+/NbuLHl+5plnzGgFNw+mUyNp0vUpSMEvUK5cOXn11VdtG6pTYs2bN882n5Vh6tSpMmzYsAzrj1j7Mnv2trB5Zvl5DwEEEEAAAQQQQAABBBBAAAEEEEAAgXAUyJKAQDjCZWWfdP0FK9WuXdvatH3WqZg0IKDTtvz0009y++2325ZxK4MubDxhwgRJTk52q8oM9Rw8eDDDa14Ep4CuXVGgQAHbxsXExNjmSZ9h3Lhxkj5Qln4f2wgggAACCCCAAAIIIIAAAggggAACCCCQuQABgcxdgurd2NhYT3t8mV5J13Kwki4wnJUBgaioKFm6dKmsXbvWaoIrzzrqQYMM6fvmSsVUkq0CFy5cMMfXwNWgQYNs2/Lzzz+bPDotlpMg2bvvvmtbJxkQQAABBBBAAAEEEEAAAQQQQAABBBAId4GABwROnjxp5r/XC36+LDB68803h7u94/7dcsstnrwJCQnywAMPeF5729i1a5dnd5kyZTzbWbVRrVo10YebqV+/fgEbdeBmO6nLN4Hdu3ebAhs2bBB9OE26Dsk999xjm3306NGio1ZICCCAAAIIIIAAAggggAACCCCAAAIIRLJAQAICugCuTukxduxYSX9R2hdoLt79rtWkSRPRKVVSU1Nl4cKFjgMCurCwlSpWrGht8oxA0AlcunTJtKlYsWKi6w7YJR15YpWxy8t+BBBAAAEEEEAAAQQQQAABBBBAAAEEEPhfAdcDAjqdS7169WTnzp0Y+yFw1113Sd26dc00KLowcM2aNeXGG2+ULl26yIcffiiTJk2Snj17SqlSpbzWPnv2bDOHv2bSKYdKlizpNT87EQgGgapVq0qvXr1sm9KpUyc5ffq0bT4yIIAAAggggAACCCCAAAIIIIAAAggggMDvAq4HBJ588slMgwF58+aVXLly/X5ktjIV0ECKPj799FOzXxdl1fnyb7jhBvP6wIEDct9998mSJUtE76b+Y9L9Ol/6m2++6dn1yiuviM7pT0IAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIXAFXAwLbtm2TadOmeTQbNWokb731llSvXt1MeePZwUYGgb/+9a8yf/582bx5s5w/fz7DPl13Yfv27eZh7VBnnQ7oqaeest4yz82bN5evv/46w3tqr0EaEgIIIIAAAggggAACCCCAAAIIIIAAAggggEBkC7gaENAFb62kC9/OnDlTcufObb3F8xUEhg0bZvboGgFbt26VdevWZXgcP378TyV1GqE/Jl27IX3SKYc+//xziY6OTv822wgggAACCCCAAAIIIIAAAggggAACCCCAAAIRKOBqQEAX+rTS448/TjDAwnD4rAsHV6tWzTwee+wxT6l9+/aZAMFPP/3kCRTExcV59lsbulaApgIFCoj6Dx8+nHNg4fCMAAIIIIAAAggggAACCCCAAAIIIIAAAghEuICrAQG9w91Kd955p7XJ81UKlC1bVvTRqlUrrzV169ZNnn/+ealfvz5TNHmVYicCCCCAAAIIIIAAAggggAACCCCAAAIIIBB5Aq4GBBo0aGAWtFXGU6dOyXXXXRd5otnY46ZNm2bj0Tl0JAhcunRJFi9ebNtVXRibhECoC+jvsfXr18vly5cdd6VcuXJSunRpx/l9ybhx40bJbAq5K9Who87q1avHtHFXAuJ9BBBAAAEEEEAAAQQQQAABBCJQwNWAQMOGDT2Eq1evlvj4eM9rNhBAIHQFjh49ahqvi14z+id0zyMt901Ag6zLly/3qVDevHnl4MGDUqRIEZ/K2WX+7rvv5P7777fL9qf9/fv3l9dff/1P7/MGAggggAACCCCAAAIIIIAAAghEpoCrAQGd1qZu3bqiwYDBgwdL27ZtJVeuXJEpS68RCCOB5ORkT2+cLBSu04elpaV5yrCBQCgK6PotmnLmzGl7l72OIrhw4YKcO3dOjhw54npA4JdffjFtiYqKcvR79eLFi6Ijeqw+mML8gwACCCCAAAIIIIAAAggggAACES/gakBANefMmSO33Xab6JQhLVu2lNGjRzNSIOJ/zAAIFwEN8E2bNs22OyNHjpQlS5bY5iMDAqEg0LdvX7n11lu9NvXs2bPSoUMHr3nc2HnjjTfKqFGjbKsaN26czJo1yzYfGRBAAAEEEEAAAQQQQAABBBBAILIEXA0I6N2Remfw+PHjpXnz5jJv3jypUqWK3HXXXaIXMXQEgdNpFHr06BFZZ4LeIoAAAggggAACCCCAAAIIIIAAAggggAACCCAQQAFXAwI613Ljxo0zNFeDBBoY8DUREPBVjPwIIIBA4AU++ugj+fHHHx0fSKe40bnv27Vr57gMGRFAAAEEEEAAAQQQQAABBBBAAAEEAiPgakAgME0M71pTUlIkKSkp4J0sXbp0wI/BARBAILwFdFqcJ5980udOTp8+nYCAz2oUQAABBBBAAAEEEEAAAQQQQAABBNwXcDUgEBsbK61bt3a/lWFc4/z5881aC4Huoi54SUIAgfAXOHPmjCxevNgsKOu0t9dff73UqVPHNruO+LLSHXfcIfny5bNeZvp8/PhxWblypVlsN9MMvIkAAggggAACCCCAAAIIIIBAkAnoNbRFixbJ6dOnHbcsb968Zsr06Ohox2XIiEB2CbgaEKhcubL85z//ya6+hORx+aAIydNGoxEIWoHu3bvLpEmTfG7f6tWrHQUFrIrbt28vN9xwg/Uy0+etW7eagECmO3kTAQQQQAABBBBAAAEEEEAAgSAU0LVRH3/8cZ9bNnz4cOnbt6/P5SiAQFYLuBoQyOrGh8PxmjRpIu+//775wEhOTvZ0SQMFOXLk8LxmAwEEEHAicOjQIZMtT548onco2KUTJ06I3v1glbPLz34EEEAAAQQQQAABBBBAAAEEwlnA+vs4Z86cUrBgQduu6kiC1NRU/q62lSJDsAhkeUDAuvh0zTXXBItBtrZDF9zs1q2bNGjQQO69917PegJt2rSRadOmZWvbODgCCGS/wKxZs6Rjx46i6404SZcuXTLZHnroIdHPEbvUpUsX0al9SFkroL8L69WrJ3v27HF04LS0NEf5yIQAAggggAACCCCAAAIIIOCOQNWqVWXQoEG2lb311ltm6l7bjGRAIEgEAn4Lul7Ufuyxx8wF72LFiokGAvQCuJUGDx4sPXr0kG3btllvReRztWrVzPxkRYsWNf3XRTg/+uijiLSg0wgg8LvA999/b+YtvHjxojh5sF7I73bBvKW/83bu3OnonOp5JyAQzGeTtiGAAAIIIIAAAggggAACCCAQOgIBGyGwdu1a6dWrlyxdutSrxubNm2XGjBlm2pzXXntN+vfv7zV/OO+sVKmSvP322yaAov1Uv9tvv10qVqwYzt2mbwgg4EXAusBfq1YteeSRR7zk/N9devfCuXPnbPORIXsFrPOqU8MNHTrUtjEaXE9ISLDNFw4ZdJTLSy+9JLt373bcHZ1mT28+aNasmeMyZEQAAQQQCB0BXeto1KhRZjoGp62OjY2VYcOGOZrqwWmd5EMAAQQQQAABBMJBICABgYkTJ4pOQ+Hkjsa9e/caR704ohcAdOoKXYQjUtOjjz4qn332mXzzzTdy9uxZ6dOnj8yePTtSOeg3AhEvoNOKaSpUqJBUqFDB1oOFym2JgiKDdV712cl51fMfKWn9+vV+fQ/4+eefCQhEyg8J/UQAgYgTGDFihOgIal9T48aNRadRJCGAAAIIIIAAAgj8LuB6QEDv6OvZs6cnGBAfH29W5q5du7Z07tz5TwtsPPfcc6LTBu3YscO06s0335TWrVtL/fr1f29lhG198MEHUrlyZdFFhufMmWPuClU/EgIIIIAAAuEuoItxWUm/D9ilffv2md+T6cvZlWE/AggggED2Cuh0eHrTk94A5SRZo8Zuvvlm83eSXZl58+aZKRf53WAnxf70ArqI6KJFizzXMtLvu9J2XFxcRF+7uJIL7yOAAAIIBLeAqwEBHRHQqVMn8+VLu/2Pf/xD9AK/rsqtKSYmxjyn/6d9+/bmro2//e1vMmXKFLNryJAh5g759Pkiabt06dLm7sju3bubbr/66qvy1VdfRRIBfUUAAQQQiHABnU5JpwGySwsWLIiY6ZTsLNiPAAIIhIrAmDFjzN+KvrZXF3fUvzft0ooVKzx/k9rlZT8CloBOz7l48WLrpePnPXv2yI033ug4PxkRQAABBBDIbgFXAwLbt2+XH3/80fTpgQcekJEjR4r+QW+XNGAwadIk2bRpk+hUAd9++60cPXpUrr32WruiYbtfF16+4447TP+sqSXCtrN0DAEEEEAAAQQQQAABBCJGICkpyfQ1T548UrRoUdt+Hz582Ke7tm0rJAMCmQgcOXLEvFukSBHJly9fJjkyvnXw4EHzhl67ICCQ0YZXCCCAAALBLeBqQGDdunWe3urClk6CAVYBveitIwp07QFNOiw0kgMC6nHLLbdYPDwjgAACCCCAAAIIIIAAAmElUL16denfv79tn/RvRF1rjoRAVgjoVMe6/oRdevDBB+2ysB8BBBBAAIGgFLC/fd+HZlsBAb3jv2bNmj6U/N+sOgTUStY8kdZrnhFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMB/AVdHCJw4ccK0RIfX6fBPX9P58+c9RaKjoz3bbCCAAAIIIBAqAnoXY/78+b021/p96TUTO0NSYOLEifLZZ5/J5cuXHbdfRwTqNItMEeiYjIwIIIAAAggggAACCCCAAAJ+CrgaEKhWrZppxqlTp2Tfvn1StmxZn5pljTDQQlWqVPGpLJkRQAABBBDILoGUlBTPoZcuXerZZiPyBIYMGWKmPfSl53PnzpUePXpI+fLlfSlGXgQQQAABBBBAAAEEEEAAAQR8FnA1IKBzQFppwoQJ8vLLL1svbZ8vXrwo48aNM/ly584t8fHxtmXIgAACCARSYMqUKVKsWDGvh/j555+97mdnZAikpaV5OvqXv/xFChYs6Hmd2Yb+3KxcuTKzXbwX4gL6fUbTHXfcIaVLl/baG807bdo0k+fSpUte80bizjVr1khCQoLj0RY6wqJ+/fqS/vtoJLrRZwQQQAABBBBAAAEEEEDAm4DrAQGdLujs2bPy2muvSbNmzaROnTreju/ZN3DgQPNHn75Rq1YtYcogDw0bCCCQhQIXLlzwHE0/l0gI+CrQtGlTueGGG7wWmzdvHgEBr0Khu9Oa9qdBgwaiD29JR5ZYAQFv+SJxnwZI7rzzTvOd0pf+X3PNNXLs2DFfipAXAQQQQAABBBBAAAEEEIgoAVcDAnpHpM6B2717d9GLarfffrv885//lJ49e0revHkzhd21a5c8/fTT8u2335r9Ggj417/+lWle3kQAAQQCLZCamuo5hE57pouke0snT56UI0eOeMuS6T49zq+//prpvvRvnjlzJv1LthHwSeDcuXOOfs60Uv09XahQIZ/qJ7N7Avo5UqRIEdsKNeCgI5dy5MhhmzeUM+j3SL3BRFOZMmUkJibGa3d0Har9+/fL8ePHveZjZ2AE9Hdh+qnT7I7C542dEPsRQMAtAQ0Sp/9+761ea5SftzzsQwABBBBAIBwEvF/p8qOHTz31lPz3v/+Vr7/+WvSPs759+0r//v2lRo0anotmmzZtko4dO8pPP/0k27dvl/RTLfTr10/q1q3rx5EpggACCLgr8NJLL8l1113ntVL9rPvwww+95km/8/Dhw+blhg0b5Prrr0+/i20EXBPQC6OavvzyS/NwUrFeYJ49e7boCAdS1gikX3hYb6JwmurVqxdRI0z0u6Td9Es6DVefPn2cEpLPRYEZM2ZIu3btHE/tpIfWz5svvvhCWrVq5WJLqAoBBBDIKDBs2DB58cUXM77JKwQQQAABBBCQgNxeNn78eGnRooWHVyPtOg+sdafX1q1b5dNPPxV9toIBesdb586dZdCgQZ5ybCCAAALhJsBUFuF2RoOzPydOnPC5Yfr7eO3atT6Xo4D/AnrjhD9Jv1OREAgWAV/WebDazOeNJcEzAggEUoDfl4HUpW4EEEAAgVAWcH2EgGLExsaaUQI6DVDv3r1l27ZtXo1q1qwpo0ePloYNG3rNx04EEEAgXAR0OiInC68PHjxYEhMTA9Zta4oHvQP3jTfe8HqcUB9GraPX3nzzTXHaj6SkJK8eobDz1ltvlb///e+2TX3rrbdk8+bNtvnIEDgBnWLRbu2JPXv2mKkYA9cKakbAf4HatWtLjx49bCt45513ZP369bb5yIAAAsEhoN9X58+f71Nj7rrrLnn99dd9KhPIzDr6sW3btraH6Natm+PvibaVkQEBBBBAAIEgFghIQMDqb5MmTeSee+6RjRs3yo4dO2Tnzp3moUPk4+PjpUKFCuah0wmF+1y4lgnPCCCAgAro2gTXXnutLYbdGga2FdhksAICOn1buKexY8fKkiVLwr2bGfqXO3duRz9nuXLlylCOF1kvoIvh2n0mMMIo688LR3QuoJ8jdj/DWhufN85NyYlAdgvo3+2vvfaaZ1S/0/b8+OOPMmTIENv1X5zWd7X5dN0SJ59PV3scyiOAAAIIIBAqAgENCCiCLgJXq1Yt8wgVFNqJAAIIRJrA/fffLzpqwVtKTk6WqVOnessS1PusKep0nRoNRNuljz76yKc5se3qY39wCJw6dUomTJjg0wKo+fPnly5dupiFl4OjF7QCAQQQQACBrBGwvj/99a9/NYvaezuqBq7/85//mCxWOW/52YcAAggggAAC2SMQ8IBA9nSLoyKAAAII+CJQp04d0YVKvaVff/01pAMCVt9uvvlmad68ufXyis8aECCFn8Dbb7/t13pFqamp0qtXr/ADoUcIIIAAAgg4EGjUqJHExcV5zblv3z5PQMBrRnYigAACCCCAQLYKEBDIVn4OjgACCCCAAAJZKaAjBDQVLlxYypQpY3vovXv3io6O0Qfp6gR0/Q69WOTkrlFrOrOrOyKlI1Hg3LlzcuDAAccjvE6fPm1GNOsUa06STnN64403SnR0tJPs5EEAAQQQQAABBBBAIOgEXA0IXLp0SfRLuBupQIECblRDHQgggAACCCCAwJ8EqlatKn369PnT+39845VXXpG1a9f+8W1e+yGg003Mnj3bj5IUQcC5QPXq1c2aZc5L+J6zTZs2MmPGDN8LUgIBBBBAAAEEEEAAgSAQcDUgoIs1Nm7c2JVu6QJGJAQQQAABBBBAAIHwENi0aVN4dIReBK3AhQsXAh4M0M5v3rw5aA1oGAIIIIAAAggggAACdgKuBgTsDsZ+BBBAAAEEEEDATQGd7kPT9u3bpUqVKrZVHz582DYPGQIroGsx3HrrrV4Pouf173//u9c87ETAm8CwYcOkdOnS3rLI8uXLZfTo0SbP5MmTRacD8paWLl0qY8eO9ZaFfQgggAACCCAQgQK6qLqmiRMnyrx58xwJFC1aVKZMmWL7fcVRZWRCwEeBoAoI6EKP5cqV87ELZEcAAQQQQACBSBVITEw0XT9//jx37YbID4HO1Z4vXz6vrdVpKEkIXI1Anjx5bH/O0q8boD+TdgGB9Pmvpm2URQABBBBAAIHwEvjtt99Mh44ePSr6cJrmz58vXbp0cZqdfAi4JuBqQOCOO+4Q6049by3U6YB0Ub8jR47IihUrZNSoUbJt2zY5efKkuUvnpptu8lacfQgggAACCCCAgBGwphgsVqyYdOzY0Vbls88+E+sLu21mMoStQGpqqowZM8anP9hiYmLkb3/7m6PFqMMWjo4hgAACCGSJgAbGdUSSL99ZcubMKY8++qjExcVlSRs5CAII/C5g/U1Ss2ZN0WujdklHBug10bS0NLus7EcgIAKuBgT0rpr8+fM7aqguGlyiRAmpVq2atG3bVv7yl7/IsmXL5P777xcdjnvDDTc4qodMCCCAAAIIIICAfv9wso6RLmrryx/XyIanwMyZM6V3794+d27Pnj3yySef+FyOAggggAACCPgiMHfuXHn22Wd9KWLybtmyRaZNm+ZzOQoggIA7AqVKlXL0N8msWbNMQMCdo1ILAr4LuBoQ8P3w/1tC583SYTI6XdDu3bulR48e8uWXX/pbHeUQQAABBBBAAAEEELiiwNmzZ80+nVamevXqV8xn7fjll1/k0KFDcubMGestnhFA4A8Ce/fuFWsO5T/s+tNL/f9EQgCBKwtYv29y5colesexXdq/f78cOHBArN9vdvnZjwACCCAQ2QJBERDQU6B/kOnoAF2AY9GiRaLDbaKioiL77NB7BBBAAAEEEEAAgYAJxMbGSv/+/W3r1++nX3zxhW0+MiAQqQIrV66UBg0amL/hItWAfiMQCIEiRYo4+j01depU0QcJAQQQQAABJwI5nGTKqjz33XefOdTx48dZGDCr0DkOAggggAACCCCAAAIIIHAVAjrK25o/+SqqoSgCCCCAAAIIIIBAFggEzQgB7Wv6Iabr16+XKlWqZAEBh0AAAQQQQAABBBBAIHQEHnvsMc+IWiet1lG3OhL33//+t5Ps5EHAbwFdI04XQrVLL774omzbts0um9/7k5OTTdlnnnlG9FhOUoUKFUTXmdGR6yQEEEAAAQQQQCCcBYIqIPDf//7XY61fyEgIIIAAAggggAACCCDwu8D58+dl0qRJv7/hcOvjjz8mIODQimyhL3Du3DnTCb3hLP1NZ956lpiYKDt27JBq1ap5y8Y+BBBAAAEEEEAg5AWCIiCgw0vfe+89c6eTiurCOXwRC/mfLTqAAAIIGIGLFy9Kr169HGnkyJFD2rdvL/Xq1XOUn0wIIJB9Alu2bBG9yKz/x52ko0ePOslGHhuB9NOydO3aVYoVK+a1xOHDh2X8+PFmOhdfPosfeughadiwode62YlAsApY/09atmwplStX9trMtLQ0GT58uMljlfNagJ0IIIAAAggggECIC7gaEPjtt9/k+++/d0SifzxeuHBBjhw5IrNmzZJly5Z5yjVq1Ehy587tec0GAggggEDoCRw4cMA0+tKlS/LOO+847oAuTLh8+XLH+cmIAALZIzBw4EAW2s0ees9R9QaaMmXKeF5ntpH+O7Yvn8WLFi2StWvXZlbZctGCAABAAElEQVQl7yEQMgLly5eX+vXre22vfk8hIYAAAggggAACkSTgakBA7xTTOzuvJhUpUkQ++uijq6mCsggggAACQSBg3TWsc1ffeeedti06ePCgGaqfkpJim1cz6F18Gjxwmt/plAGODk4mBEJEYOPGjeLLnfkxMTFy6623Ss6c9l8Rrf97N954o+jDLi1cuNAuS9js15teVq1a5Xj0hHY8NjbW9k5mf4Csz2Ite9ddd9lWoSMKdG53/blxcs727dtnWycZEAg3gT179ogvP/v6XahWrVpSsGDBbKfQ9RV++ukn0ZERdokRE3ZCvu3fu3ev6MNJ2rx5s5Ns5EEAAQQQQMAvAfu/9vyq1r9CugjVhx9+6OiPSv+OQCkEEEAAgawWiI6Oln/84x+2h503b54JCNhm/L8Mb7/9tjz33HNOs5MPgYgT0FGb99xzj8/97tu3r2f6DG+F9QKXpgYNGsjDDz/sLavZt3jxYkcXoGwrCoEMvXv3drSw6h+78uOPP9rezfzHMr68dvJZPHnyZBMQ+OWXX6Rx48a+VE9eBCJC4Ndff5WKFSua0e6+dPjuu++WBQsW+FIkIHnbtm0r+p2LlLUCSUlJ5udG14EhIYAAAgggkN0CrgYE9O5+J3ceWZ3Wi0R58uSR66+/Xpo2bSqtWrVydEeaVZ5nBBBAAIHIFdCLVZp03QFde8YuWXcz2+VjPwLhImD9H9EL906mYtQ7yfVhlQsXh+zoh2WoIy18GW2hi5raTW8S6P7o3cOanP7c6MUt7iIO9Fmh/mAS0GlydRSQJv1b1i7plESpqalB89lqfT7piDD9e9xb0v/bXMD2JuR8nwYELEsnPzf6M8N0Vs59yYkAAggg4JuAqwGBGjVqyA8//OBbC8iNAAIIIIDAVQjoujN6N65devTRR8W60GWXl/0IhJNA6dKlHa3joQvPfvXVV+HU9Wzvi96J+8gjj9i2o1u3bqJ3HQdTKlmypIwePdq2Sf369fNpdJdthRGc4ZNPPhH1TD/Nkx2HTjWld3vf6GDaLru62O+7gI6osQv6zZ4920yJu3v3brnmmmscHUTr1IWOu3Tp4ii/P5mefvpp2ykddVqh1q1b+1M9ZbwITJw40fZmlnHjxpm1Fr1Uwy4EEEAAAQT8FnA1IOB3KyiIAAIIIIAAAggggAACCESwwKxZs+TIkSM+CZw4ccKsp0NAwCe2LM186NAhczy9uK7ny2nSn4dABgSctoN8CCCAAAIIIBB+AgQEwu+c0iMEEEAAAQQQQAABFwXOnj1ralu9erWjC3Tr1q1z8eiRXdXcuXNl+vTpjtafCPXpNaypl+68807R+ebt0r/+9S85duwYUzbZQQXJ/qJFi0qvXr1sW6PrvyxatIjzaitFBgSCQ0D/z06ZMsXR7ymrxTfddJMMGDDATM9nvcczAgggkJUCBASyUptjIYAAAggggAACCIScwJ49e0yb9+7dKzq1EinrBHRKuK1bt2bdAYPgSMWLF5fq1avbtsTJ2iC2lZAhywR0vSMn53Xz5s1Z1iYOhAACVy+gU70lJCT4XFGLFi1Ep90mIYAAAtkh4GpAQO/KOXfuXMD7UaBAgYAfgwMggAACCCCAAAIIIKACOtWHpuuvv15q1qxptr39s2DBAs+io97ysc9ewFq8Vd3V31vSv0V0Pn0SAggggAACWSVg/Z6qVq2a6Po7dumbb74xWaxydvnZjwACCARCwNWAwJIlS6Rx48aBaKenzqioKM8fZZ432UAAAQQQQAABBBBAIEAC+v1TU1xcnOgCwHZp+fLlBATskHzcr1Po6CLy3pJO7URAwJsQ+xBAAAEEAiWg073dc889ttV/++23TAlmq0QGBBAItECOQB+A+hFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCD7BVwdIXDNNdfIvffea6KduhDSxYsXM/Tw2muvlbJly0pMTIz88ssvcvjwYSKjGYR4gQACCCCAQPgJ6B1TS5cuddQxa1FNR5nJhAACCASxgI5WaNOmjViLUts11Zqayi5fVuw/ffq01K1bV3bs2OHocHx2O2IiEwIIIBBUAosXL5aWLVuKfuY7TXnz5pVp06ZJ8+bNnRYhHwIIBKGAqyMEdJGk7777TurVq+cJBtSuXVu++uorOXPmjBw5csQstrJixQo5ePCgWW9g1qxZGeZi/ec//2k+jPQDKbNHcnJyEDLSJAQQQAABBBDITEB/l+sfG3qhy8mDi0qZKfIeAgiEooDeIKWfgU4++4IpGKDWu3btkm3btjluO5/dofgTSpsRQCDSBXTa71OnTjn+rNffVXpt74cffoh0OvqPQMgLuDpCQDVGjhwpb7zxhoF599135emnn74iUu7cuU00UldX15XZR4wYIYMGDZJatWpJ06ZNr1iOHQgggAACCCCQfQLTp0+Xzz//3NEov9TUVE9D9Xd9bGys53VmG6tWrTJ1Z7aP9xBAAIFQFKhSpYp07tzZtumvvPKKBOPNT8OGDZMcObzfR/bll1+Krp1BQgABBBAIHQErmHvzzTdL165dbRs+ZcoUWbdunW0+MiCAQPALuBoQOH78uPTt29f0+rnnnvMaDEhPowu16RfNtWvXyvfffy+9e/cmIJAeiG0EEEAAAQSCSKB///6ye/dun1uk0waWLFnSa7l9+/Z53c9OBBBAINQEChQoIPHx8bbNjo6Ots2THRnKly8vdm0rUqRIdjSNYyKAAAIIuCCQL18+R7+nChYs6MLRqAIBBIJBwNWAwLJlyzx3C77wwgs+9U/vOnnsscdMQGD79u3y66+/ynXXXedTHWRGAAEEEEAAAd8FrDV/9I6fyZMn21Zw4sQJk6dBgwZSokQJr/lTUlJkzpw5XvME805fbXR6kEAnvZvLyXnibt1AnwnqRwABBBBAAAEEEEAAAQRCT8DVgIC1YKDeAVO8eHGfNXQNAitpXboIFwkBBBBAAAEEAiuwf/9+c4AZM2aIPpwmXSy4fv36XrPr6MFQDghYNv/5z39EH9mZdC0mTRoQePTRR7OzKRwbAQQQQAABBBBAAAEEEEAgRAVcDQgcO3bMMCQlJZk/VnUqIF+SLlxlpcKFC1ubPCOAAAIIIIBAAAWsu+ALFSokToYCHzhwIICtCa6qLRt1UR+7FEibc+fOeQ5vN/WSZtRgzNmzZz1lnG5on3XBOLtk2djlY79zAR1RY2evecIhXbhwwbav2s9gW2w31O3tfr6s/qX/vLHe4xkBBBBAAIFgEtCbZHz5rqvXKHV6JBICCIi4GhCIi4szpjqVwOrVq6VevXo+Gc+cOdPk1/+kdevW9aksmRFAAAEEEEDg6gRatmwpDz30kG0lrVu3jriLdGrTrl27oLDR70ljxoyxbcu7774rCxYssM1nZTh48KDZDIbREFabIuX55MmTpqs6faY+wjkdOnTIdG/o0KGiD1LWCeg0bytWrMi6A3IkBBBAAAEEAiSggYAqVarInj17fDpChw4dRBdHJiEQ6QI53AS46667PNV16tTJ3JnmecNm48MPP5SpU6eaXLfccoswQsAGjN0IIIAAAggggICLAtZITxerpCqHAqmpqQ5zhn62rFhnI/SV3O+BjughGOC+KzUigAACCGSPgI7K9TUYoC1dsmRJ9jSYoyIQZAKujhDQeYT1oV82d+7cKY0aNZLBgweL3kmoiwZnlhITE+WNN96Qf//732a35nvrrbcyy8p7CCCAAAIIIIAAAgEWqFWrljzxxBO2R3nhhReEi7u2TD5lUHf195b0jri+fft6yxIS+5o2bSotWrSwbeszzzwTcSOSbFH8yKDTKljppZdeErtpxxISEmTcuHFWEZ4RQACBoBE4deqU9OzZU3799VfHbcqZM6c8//zzcs8999iWWb58ubz22mviNFjvz0Vp20aQwSeBESNGSN68eb2WWb9+veiNyOl/H3otwE4EwlzA1YCAWr333nsmEKB/IG7evNlMPXDTTTdJjRo1pFSpUuahc1fu27fPPHTxYJ1D1EoDBgyQ+++/33rJMwIIIIAAAggggEAWCujcqnYXC7U50dHRWdiqyDhU0aJFbe3DJQij63I4+TnzdU2yyPhJ8b2X6R2LFy9ua88FLt+NKYEAAlkj8MMPP8jkyZN9PlhMTIyjgMDYsWPl66+/9rl+CmSfgH6fsAsI6M3IJAQQ+F3A9YCAXvifMWOGPPzww2LNh7pr1y7Rh7ekf1R269ZNBg0a5C0b+xBAAAEEEEAAAQQQQAABBBBAIMIEzp8/b6YZdrqIqN01iAjjC5vu6hRomvLnz29mo7DrmN4ZvmHDBrHK2eW38ulU1rVr17bLbuajT0tLs81HBgQQQCCYBFwPCGjnHnjgAdm2bZsZzjxt2jTboVZ33323jBo1SqpVqxZMNrQFAQQQQAABBBBAAAEEEEAAAQSCQEBnI+jdu3cQtIQmBIOABgTatGlj2xS9wK8BAV9ThQoVHNWva2ESEPBVl/wIIJDdAgEJCGinrr/+epk0aZKMGTNGvvnmG7OuwOHDh+XIkSOiw1Tj4+PNo1KlSrZzpWY3EsdHAAEEEMg6AZ2v89ChQ7YH1OnnSAgggAACCESqgDUP8vHjx21/b1p3vEaqVSj2OyUlxfa8Wv3SqTKKFClivQypZ50GLTk52VGbf/nlF5NPLwSXKFHCtszu3bvl0qVLtvnIgAACCCCAQKQJBCwgYEEWKlTITB+kUwiREEAAAQQQuJKAtTDYpk2bHP2Rd6V6eB8BBBBAAIFIEDhx4oTpZo8ePUQfpPAQ2L9/v+nI3LlzHX8f0jUipk+fLm3btg0pBP3OV7duXdHghy9Jp3LRtQft0uOPPy7Hjh2zy8Z+BBBAAAEEIk4gR1b3WL+46l0sJAQQQAABBNILHD16NP1LthFAAAEEEEDAi4COqCOFn4AV6PGlZzpaZO3atb4UCYq8W7Zs8TkYEBQNpxEIIIAAAgiEuEDARwjoGgJz5syRnTt3mode8HnooYfMHQxqN3jwYPntt9/k2WeflYoVK4Y4J81HAAEEELhagTJlypjfDXb16O8Pa+i4XV72I4AAAgggEK4C3bp1k1tvvdVr9y5cuCBPPfWU1zzsDC6BmjVryjPPPGPbKJ2iNyEhwTZfMGeIjY2VYcOG2TZx6NChsmPHDtt8ZAgtAZ3WqWvXrrJ161ZHDfd11MepU6dMvcuWLZP69evbHkOvXZEQQACBcBcIWEBA71Do1auXLF261Kvh5s2bZcaMGfL+++/La6+9Jv379/ean50IIIAAAuEtEB0dLUWLFrXtpOYjIYAAAgggEOkCBQoUsP29qQEBUmgJxMTE2J5X7VGuXLlCq2OZtNbpdz81IYWfwK5du2T8+PEB61hiYqKp++TJk7Jy5cqAHYeKEUAAgVASCEhAYOLEidKlSxdHK63v3bvXeOkwx5deeslMJzR8+PBQMqStCCCAAAIIIIAAAggggAACCCCAAAI+CqRf+FnXfciZ0/tlqhUrVsiGDRscH8VagL106dLStGlT23JTpkyRM2fO2OYLxgza1wkTJoi1NptdGxcvXmyXhf0IIBCmAt4/af3o9O7du6Vnz56eYEB8fLzoh3rt2rWlc+fOcujQoQy1Pvfcc2ZqCGvo35tvvimtW7d2NJQrQ0W8QAABBBBAAAEEEEAAAQQQQAABBBAISYEHHnhAcufO7bXterHbl4CAVVnx4sWlWbNm1ssrPn/xxRchGxCYP3++uTn3ip1jBwIIIPB/Aq4GBNLS0qRTp05y+vRpU/0//vEP0Qv8VoQ3syF+7du3N2sK/O1vfxONxGoaMmSIfPPNN2abfxBAAAEEEEAAAQQQQAABBBBAAAEEQkMgJSXF8Vpf1qwRodGz4G6ldS1Or71VqlTJtrF6Q69VxjYzGRBAIKwEXA0IbN++XX788UcDpJHdkSNHSo4cOWzBNGAwadIk2bRpk6xfv16+/fZb0cWHr732WtuyZEAAAQQQQAABBBBAAAEEEEAAAQQQCA6BOnXqiK4XScoegcKFC8srr7xie/CXX37Zr9EWthWTAQEEgl7A/mq9D11Yt26dJ/egQYMcBQOsAlFRUaIjCqykkUoSAggggAACCCCAAAIIIIAAAggggEDoCGzZsiV0GktLEUAAgQgUcHWEgBUQ0Dv+a9as6TNn1apVPWU0IFC3bl3PazYQQAABBBBAAAEEEEAAAQQQQAABBK5OYPbs2TJgwABJTU11VNGxY8ckOTlZSpQoIZlNBf3HSqyFfF977TWJi4v74+4Mr9euXWumms7wZhi/sGwefvhhyZs3r6OeNmrUSD744ANHeQOZSX8ONH3yySfy9ddfOzqUzvzx6aefii7qTEIAgeARcDUgcOLECdOzfPnySZ48eXzu5fnz5z1loqOjPdtsIIAAAggggAACCCCAAAIIIIAAAghcvcDkyZP9mirm559/9ungukCw3UXvXLly+VRnuGT2Ze2ErVu3yqhRo0SvtWVn+u2338zhNTBgBQectGfBggWi64aSEEAgeARcDQhUq1bN9OzUqVOyb98+KVu2rE89tUYYaKEqVar4VDbcMmvUeOHChbJy5UrR4Xb6y+LMmTPmce7cOcmfP78UKlRIdG64okWLSo0aNaR+/fqic/XpPhICCCCAAAIIIBCJAnrHo13auHGjXRb2I4AAAgggELYC1l3qtWvXlttvv922n6NHj5ZLly7J/fff72ix2n/961+2dUZ6hkceeUSuu+46rwzHjx+XiRMnmjw6Lbdd8ETX9cyKVKtWLdFRC3ZpwoQJojcOz5gxQ3bu3GmX3ezXESVPPPGEo7y+ZLJ+5k+ePGlGx9iVXbZsmV2Wq9pv3RD9xRdfiNMp08uVKyddu3a9quNSGAFLwNWAQPXq1a16Rf/j6wIlTtPFixdl3LhxJrtGkePj450WDat8+mE5fvx4GTt2rOMPTAWYOnWqcdDhe506dZJ+/fpJxYoVw8qGziCAAAIIIIAAApkJHD582PP266+/7tlmAwEEEEAAAQSuLFCmTBlp3LjxlTP8354xY8aYLb3G4CQ/AQFbUtGL6hUqVPCacdOmTZ79I0aM8Gxn94ZO/+Pk5+C9994zTZ0zZ47ow2mqV6+epJ9S3Gk5b/msERk69VUwfFc8evSoaa5OveR0+iUtoFOrp7/26q3P7EPAm4DrAQEdwnT27FnRueKaNWtm7lj31gBr38CBAyUhIcG81A/GSJwySIMoPXr0MH6Wi6/POgegzuemdWlQQD/odMFmEgIIIIAAAgggEK4C6edA1hGTdknvxLKGvdvlZT8CCCCAAALeBHREv3X38ZXyOb0D+Erlnb6vNxiuWbPGNrsv073YVkaGgAlcuHDBU7deCLa7TqYzdRw6dMhTJrs3rP8Xetd/8eLFbZuzYsUKk0evKbqdrO+KOspCrznaJf1/rbOfBCoFk02g+ki9wS3gakCgYMGCMnLkSOnevbvoB5cOPfvnP/8pPXv2vOK8cbt27ZKnn35avv32WyOlH3CRGE3+8ssvzbAoHYZnJZ1yqWHDhqLDgjQCq1ME6doMOoJCfVNSUuT06dOyf/9+M0WTfnhaQ8TS0tJk6NChkpiYKJMmTSIoYKHyjAACCCCAAAJhLfDiiy/a9k9HYs6bN882HxkQQAABBBCwE3jsscfssgR8v3X389KlS80dxAE/IAfIcoHnn3/edq1OvTFUry0FW9Kbhe+9917bZrVu3Vr0WlYgk163dPJdUadoWr9+fSCbYupu2rSp3HfffbbHadu2rejMKiQE3BJwNSCgjXrqqafkv//9rxnyonNi9e3bV/r372/muD9y5Ihptw576tixo/z000/mAnb6//B6V7tGPiMpLVq0SNq3b2/m5NN+63/0wYMHS+XKlX1m0IjwsGHDxBqaNWXKFDNSo0OHDj7XRQEEEEAAAQQQQAABBBBAAAEEEAhuAb1RkIQAAggggIBTAdcDAnpgnQP/8ccfl9mzZ5t2aBQr/bA1XSFdH+mTTmujkXWNwkVa0rUWrAVFdHSF3rXmb9JRBVr+pptukj59+phqdHG9du3aSc6cATnd/jaVcggggAACCCCAAAIIIIAAAmEikP7uVf171G7q2kBMC5IdlDq3e/ny5b0eetWqVVkyb3mdOnXk//2//+e1LbpTF2215jC3zUwGBBBAAIGwEwjIFeLY2FgzSkCnAerdu7ds27bNK1zNmjVFV63X6XEiLSUlJcmSJUtMt3XOW3VwI+lwsnXr1snkyZNFhw/qiu6VKlVyo2rqQAABBBBAAAEEEEAAAQQQQCCDgP5ta6UDBw5YmzwjgAACCCCAQJAJBCQgYPWxSZMmcs8998jGjRtlx44d5qK0XpjWxTPi4+PNiuq6qnqNGjUkR44cVrGIet6zZ49nAaIWLVq46tCqVSsTEFBQAgIR9WNFZxFAAAEEEEAAAQQQQCACBM6dO2d6qeuiJCcn2/b48OHDtnn8zWAtkqnldSpguwVQ58+fL6tXr/b3cJRDAAEEEEAAAT8FXA8I6Kr2RYoU8TQnJibGrODtZBVvT6EI2kg/11/FihVd7bkGXax07Ngxa5NnBBBAAAEEEEAAAQQQQACBMBDYv3+/6UVCQoLoI1iSrguo1wK8Jb1xkIQAAggggAACWS/gakDg1KlTUq5cObMYbufOneXRRx+1XQU967scXEeMi4vzNEjXWWjTpo3n9dVuWFMRaT3Vq1e/2uoojwACCCCAAAIIIIAAAgiErYDO8243r/ru3buDqv9paWmmPbqWXPq/La/UyIULF3pGqF8pD+8jgAACCCCAQHgLuBoQ+PTTT0VHCCxbtswsItyhQ4fw1nOhd2XKlDFBk5SUFJk+fbpZCPjaa6+96pp15MHHH39s6smdO7dUqVLlquukAgQQQAABBBBAAAEEEEAgXAX+/ve/h2zXdDFZvSHPLulNY+kX/7XLz34EEEAAAQQQCD8BVyfu37Bhg0eocePGkj9/fs9rNjIX0LUTnnzySbNT7zZp3ry57V0pmdf0+7s6j2TLli3NosL67m233WY7XPP30mwhgAACCCCAAAIIIIAAApEhcOnSJU9H8+TJI/ny5fP6yJUrlyc/GwgggAACCCCAQCgKuDpCoHjx4h6DQoUKebbZ8C4wcOBAmTBhguiUSytXrjTTLnXt2tUEBxo2bCh58+b1XsH/7NWhouvXr5e5c+fKu+++KwcPHjRlihUrJuPGjbMtTwYEEEAAAQQQQAABBBBAIJIF3njjDfO3mDeD5cuXy/Dhw71lYR8CCCCAAAIIIBDUAq4GBB588EEZMmSI6fDXX38teqe6k4vZQS2UBY2LjY2V7777Tlq0aCFJSUmSnJwso0aNMo+oqCgpUaKE6JyQhQsXNp46BVBqaqroNEM6NZAuJJWYmCjnz5/P0Nro6GiZOnWqKZthBy8QQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEIk7A1YBAjRo1ZNCgQSYooBeq77rrLnPne8WKFSMO1tcO16tXT/Rukx49epjggFX+8uXLcuDAAfOw3nPyXLVqVRkzZow0atTISXbyIIAAAggggAACCCCAAAIIIIAAAggggAACCIS5gKsBAV2c6Nlnn5X4+Hjp3bu3rFq1SjRIoAvali9f3ryvd7rrnet2qUuXLnZZwm6/Gs2bN88syDx69GjRBZ90XQGnqUiRImaaoXbt2kmzZs0kZ05XT6/TZpAPAQQQQAABB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```r

## Summarize the results. DE are the results from the original anlayses of the datasets individually.
E_results_metastats <- data.frame(DE, "DE.fishercomb_adjP"=fishcomb$adjpval, "DE.invnorm_adj_P"=invnormcomb$adjpval)
library(data.table)
setDT(E_results_metastats, keep.rownames = "Gene")  # make the rownames a part of the dataframe with column name "Gene"
head(E_results_metastats)
```

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```r
E_results <- data.frame(DE, "DE.fishercomb"=ifelse(fishcomb$adjpval<=0.05,1,0), "DE.invnorm"=ifelse(invnormcomb$adjpval<=0.05,1,0))
head(E_results)
```

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```r

## Dealing with conflicts in the sign of differential expression 
signsFC <- mapply(E_FC, FUN=function(x) sign(x))
sumsigns <- apply(signsFC,1,sum)
commonsngFC <- ifelse(abs(sumsigns)==dim(signsFC)[2], sign(sumsigns), 0)
unionDE <- unique(c(fishcomb$DEindices, invnormcomb$DEindicies))
FC.selecDE <- data.frame(E_results[unionDE,], do.call(cbind,E_FC) [unionDE,], signFC=commonsngFC[unionDE], DE=DE[unionDE])
dim(FC.selecDE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDIyICA4XG4ifQ== -->

```
[1] 22  8
```



<!-- rnb-output-end -->

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```r
  # keep the ones that don't have a conflict (1 or -1)
keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),]
dim(keepDE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE4ICA4XG4ifQ== -->

```
[1] 18  8
```



<!-- rnb-output-end -->

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```r
conflictDE <- FC.selecDE[which(FC.selecDE$signFC==0),]
dim(conflictDE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDQgOFxuIn0= -->

```
[1] 4 8
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChGQy5zZWxlY0RFKSAgIyB0aGUgb25lcyB3aXRoIGNvbmZsaWN0ID0gMFxuYGBgIn0= -->

```r
head(FC.selecDE)  # the ones with conflict = 0
```

<!-- rnb-source-end -->

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<!-- rnb-frame-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChrZWVwREUpXG5gYGAifQ== -->

```r
head(keepDE)
```

<!-- rnb-source-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChjb25mbGljdERFKVxuYGBgIn0= -->

```r
head(conflictDE)
```

<!-- rnb-source-end -->

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  </script>
</div>

<!-- rnb-frame-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxudGFibGUoY29uZmxpY3RERSRERSlcbmBgYCJ9 -->

```r
table(conflictDE$DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG4wIFxuNCBcbiJ9 -->

```

0 
4 
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyMjIE1lcmdlIHJlc3VsdHMgYW5kIHdyaXRlIHRvICBhIGZpbGVcbnNldERUKEZDLnNlbGVjREUsIGtlZXAucm93bmFtZXMgPSBcIkdlbmVcIilcbmhlYWQoRkMuc2VsZWNERSlcbmBgYCJ9 -->

```r
### Merge results and write to  a file
setDT(FC.selecDE, keep.rownames = "Gene")
head(FC.selecDE)
```

<!-- rnb-source-end -->

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  </script>
</div>

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZCh0b3AudGFibGVfRV9pbnRlcnNlY3QpXG5gYGAifQ== -->

```r
head(top.table_E_intersect)
```

<!-- rnb-source-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChFX3Jlc3VsdHNfbWV0YXN0YXRzKVxuYGBgIn0= -->

```r
head(E_results_metastats)
```

<!-- rnb-source-end -->

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```r

# merge top.tables from HS2008 and 2012 - all genes that were not originally filtered otu.
dim(top.table_Eco08)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEzMDY3ICAgICA3XG4ifQ== -->

```
[1] 13067     7
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGltKHRvcC50YWJsZV9FY28xMilcbmBgYCJ9 -->

```r
dim(top.table_Eco12)
```

<!-- rnb-source-end -->

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```
[1] 14431     7
```



<!-- rnb-output-end -->

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```r
top.table.E_All<- merge(top.table_Eco08,top.table_Eco12,by="Gene",all.x = TRUE, all.y=TRUE, suffixes = c(".08", ".12"))
dim(top.table.E_All)
```

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```
[1] 14601    13
```



<!-- rnb-output-end -->

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```r
  #merge original results with results from meta-analysis
Ecotype_results <- merge(top.table.E_All,FC.selecDE, all.x = TRUE)
Ecotype_results2 <- merge(Ecotype_results, E_results_metastats, all.x = TRUE)
head(Ecotype_results2)
```

<!-- rnb-source-end -->

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</div>

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```r
write.table(Ecotype_results2, file = "OutputFiles/Meta_Ecotype.txt", row.names = F, sep = "\t", quote = F)
```

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### Make summary tables and Upset plot
Ecotype as main effect

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```r
## Make a table of results
fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)]
invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)]
indstudy_de <- list(rownames(keepDE)[which(keepDE[,"DE.2008Dataset"]==1)],rownames(keepDE)[which(keepDE[,"DE.2012Dataset"]==1)])
IDD.IRR(fishcomb_de,indstudy_de)
```

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```
   DE   IDD  Loss   IDR   IRR 
18.00 10.00  0.00 55.56  0.00 
```



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```r
IDD.IRR(invnorm_de, indstudy_de)
```

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```
   DE   IDD  Loss   IDR   IRR 
11.00  7.00  4.00 63.64 50.00 
```



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### Make Upset Plot Ecotype

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```r
# Ecotype  2008
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_Eco08_top <- top.table_Eco08[which(top.table_Eco08$adj.P.Val<=0.05), ]
dim(UP_Eco08_top)
```

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```
[1] 7 7
```



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```r
  # converting column of "Gene" in dataframe column into vector by passing as index
Ecotype_HS2008 = UP_Eco08_top[['Gene']]
#  HS2012
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_Eco12_top <- top.table_Eco12[which(top.table_Eco12$adj.P.Val<=0.05), ]
dim(UP_Eco12_top)
```

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```
[1] 4 7
```



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```r
  #converting column of "Gene" in dataframe column into vector by passing as index
Ecotype_HS2012 = UP_Eco12_top[['Gene']]
#MetaAnalysis
## Keep the genes from the meta-analysis that have invernorm adjusted p-value <= 0.05 and the log fold change is going in the same direction in both analyses
UP_MetaA <- Ecotype_results2[which(Ecotype_results2$DE.invnorm_adj_P<=0.05), ]
UP_MetaB <- UP_MetaA[which(abs(UP_MetaA$signFC)==1),]
dim(UP_MetaB)
```

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```
[1] 11 23
```



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```r
  #converting column of "Gene" in dataframe column into vector by passing as index
Ecotype_Meta = UP_MetaB[['Gene']]

listEcotype <- list(HS2008 = Ecotype_HS2008, HS2012 = Ecotype_HS2012, MetaAnalysis = Ecotype_Meta)

# Make upset put and save as a .pdf file
pdf("OutputGraphs/Upset_Ecotype.pdf")
  #make plot
upset(fromList(listEcotype), mainbar.y.label = "Number of Differentially Expressed Genes (adjusted p-value=0.05)", sets.x.label = "DEGs",order.by = "freq")
dev.off()
```

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```
null device 
          1 
```



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Interaction between Treatment and Ecotype

first get the data ready

#Merge top tables results for each dataset - keep the genes in common ~13000
library(plyr)
top.table_I_intersect <- merge(top.table_I_2008,top.table_I_2012,by="Gene",all.x = FALSE, suffixes = c(".08", ".12"))
dim(top.table_I_intersect)

[1] 12897    13

summary(top.table_I_intersect)

     Gene              logFC.08          AveExpr.08          t.08            P.Value.08       
 Length:12897       Min.   :-9.33963   Min.   :-2.530   Min.   :-5.01137   Min.   :0.0000513  
 Class :character   1st Qu.:-0.47883   1st Qu.: 1.378   1st Qu.:-0.58809   1st Qu.:0.2712176  
 Mode  :character   Median : 0.04333   Median : 3.343   Median : 0.05626   Median :0.5205675  
                    Mean   : 0.06475   Mean   : 3.261   Mean   : 0.07265   Mean   :0.5149628  
                    3rd Qu.: 0.62289   3rd Qu.: 4.970   3rd Qu.: 0.72829   3rd Qu.:0.7589498  
                    Max.   : 8.90957   Max.   :15.915   Max.   : 5.83504   Max.   :0.9999661  
  adj.P.Val.08         B.08           logFC.12           AveExpr.12          t.12         
 Min.   :0.3459   Min.   :-4.892   Min.   :-6.779707   Min.   :-5.122   Min.   :-5.03386  
 1st Qu.:0.9994   1st Qu.:-4.731   1st Qu.:-0.208392   1st Qu.: 1.965   1st Qu.:-0.60827  
 Median :0.9994   Median :-4.631   Median : 0.002788   Median : 3.609   Median : 0.00907  
 Mean   :0.9992   Mean   :-4.637   Mean   : 0.024088   Mean   : 3.543   Mean   : 0.03445  
 3rd Qu.:0.9994   3rd Qu.:-4.585   3rd Qu.: 0.239667   3rd Qu.: 5.057   3rd Qu.: 0.68317  
 Max.   :1.0000   Max.   :-2.428   Max.   : 5.728788   Max.   :16.730   Max.   : 4.17372  
   P.Value.12        adj.P.Val.12         B.12       
 Min.   :0.000072   Min.   :0.9999   Min.   :-4.672  
 1st Qu.:0.280500   1st Qu.:0.9999   1st Qu.:-4.644  
 Median :0.528093   Median :0.9999   Median :-4.616  
 Mean   :0.522301   Mean   :0.9999   Mean   :-4.605  
 3rd Qu.:0.766334   3rd Qu.:0.9999   3rd Qu.:-4.589  
 Max.   :0.999905   Max.   :0.9999   Max.   :-3.985  

dim(top.table_I_intersect)

[1] 12897    13

head(top.table_I_intersect)

#Put p-value and Fold changes results in lists
I_rawpval <- list("pvalue_08"=top.table_I_intersect[["P.Value.08"]], "pvalue_12"=top.table_I_intersect[["P.Value.12"]])
summary(I_rawpval)

          Length Class  Mode   
pvalue_08 12897  -none- numeric
pvalue_12 12897  -none- numeric

I_adjpval <- list("adjpvalue_08"=top.table_I_intersect[["adj.P.Val.08"]], "adjpvalue_12"=top.table_I_intersect[["adj.P.Val.12"]])
summary(I_adjpval)

             Length Class  Mode   
adjpvalue_08 12897  -none- numeric
adjpvalue_12 12897  -none- numeric

I_FC <- list("I_FC_08"=top.table_I_intersect[["logFC.08"]], "I_FC_12"=top.table_I_intersect[["logFC.12"]])
summary(I_FC)

        Length Class  Mode   
I_FC_08 12897  -none- numeric
I_FC_12 12897  -none- numeric

#make matrix of 1 for genes DE and 0 for not.
DE<- mapply(I_adjpval, FUN=function(x) ifelse(x <= 0.05, 1, 0))
studies <-c("2008Dataset", "2012Dataset")
colnames(DE)=paste("DE", studies,sep=".")
rownames(DE)=paste(top.table_I_intersect$"Gene")
#DE<-cbind(top.table_H_intersect$"Gene", DE)
summary(DE)

 DE.2008Dataset DE.2012Dataset
 Min.   :0      Min.   :0     
 1st Qu.:0      1st Qu.:0     
 Median :0      Median :0     
 Mean   :0      Mean   :0     
 3rd Qu.:0      3rd Qu.:0     
 Max.   :0      Max.   :0     

head(DE)

      DE.2008Dataset DE.2012Dataset
A1CF               0              0
AAAS               0              0
AACS               0              0
AADAT              0              0
AAGAB              0              0
AAK1               0              0

dim(DE)

[1] 12897     2

#Check for normal distributions
par(mfrow = c(1,2))
hist(I_rawpval[[1]], breaks=100, col="grey", main="2008 Dataset", xlab="Raw p-values")
hist(I_rawpval[[2]], breaks=100, col="grey", main="2012 Dataset", xlab="Raw p-values")

Interaction: Calculate the meta-analysis p-value

Interaction between treatment and ecotype

library(metaRNASeq)
# P-value combination using Fisher method 
fishcomb <- fishercomb(I_rawpval, BHth = 0.05)
summary(fishcomb)

              Length Class  Mode   
DEindices         0  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric

hist(fishcomb$rawpval, breaks=100, col="grey", main="Fisher Method", xlab = "Raw p-values Interaction (meta-analysis)")

# P-value combination using inverse nomral method method 
invnormcomb <- invnorm(I_rawpval, nrep=c(12,20), BHth=0.05)
summary(invnormcomb)

              Length Class  Mode   
DEindices         0  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric

head(invnormcomb)

$DEindices
integer(0)

$TestStatistic
   [1] -0.1212853249  1.0776364974 -0.0038645567  0.8765773144 -0.6806518523 -2.0280389649
   [7] -0.8251003462 -1.5672008943 -0.7611914886  0.5325403735  0.6616605385  1.2352607511
  [13] -0.8406607812 -0.7970509285 -1.0762733467 -0.5255511135 -1.0004121062 -0.2553114831
  [19] -0.7445661654  1.0611068488 -0.6549744092 -0.6260310863 -1.9833774160 -2.1022879066
  [25] -0.3193462980  1.4407976521  0.6561135942 -0.0946186871  0.4495768181  0.7803960548
  [31]  1.0863053083  0.9160470871 -0.2867475548  2.7518109506 -0.3528943525 -1.9989825180
  [37] -2.3428234606 -0.3565562466 -1.1658460540 -1.7939177677 -0.3347070710  2.0407029074
  [43]  0.7892540724  0.7684016948  1.3638510032  0.3102025947 -1.6341755235 -0.6457444353
  [49] -1.1158994614 -1.0710698107 -0.3065177419  0.6655432968 -0.1921421948 -1.2413588830
  [55] -0.6896953096 -2.7146296594  0.1833714156  1.1861898903  1.6348669703  0.2957997343
  [61] -0.4434878354 -1.2516159991 -1.0366240020  0.3794833080  0.1696309221  0.0040407329
  [67] -1.5731822662  0.5867539864  0.5356203755 -0.2710013152  2.0750118728  0.2921689984
  [73] -1.5019413035 -1.0505494510 -0.5406090063 -0.3288304205  0.0348736809  0.5475496179
  [79] -0.1754444540  0.0849796004 -1.1518342267  0.1632009949  1.6598995859  0.7170049578
  [85] -1.3359531758 -0.2319308233 -1.2287097801 -0.3481054542  0.0011893258  1.5296372564
  [91] -2.0024793178  0.7588810778  0.5826856643  0.9934698416 -0.3435743016 -0.6014589495
  [97] -0.6667925674 -2.5112122204 -0.3953924103  0.9924320332  0.1205149198 -0.8550020843
 [103]  1.8116640127 -1.9839852803  0.0939350765  0.8970514825 -0.0101058128 -0.5339475414
 [109] -0.5829613715 -0.1900139429  0.7611279563  1.9381156197 -0.6534439736 -0.7931041545
 [115] -0.3158135724  0.6674315841  0.7734201448  1.4629717956  1.1065357853 -1.5114760637
 [121]  0.8998734092 -0.8384883469 -0.1565911207 -0.8715244944 -1.2906252744  0.4404930401
 [127]  0.6519496123 -0.2649999256 -1.3799130215 -0.2449448663 -0.4044878638 -0.1637739392
 [133] -1.2908556898  0.7364561071 -0.8438712117  0.1013317343  1.1353818863  1.9563144677
 [139]  0.1482344145 -1.2964362894 -0.5380094887 -0.3305787412 -0.2237092897 -1.3296662902
 [145] -0.9907699336  2.2742723362 -0.4971235847  0.4012384585 -0.2387204331  1.3913348150
 [151]  0.7943875596 -0.0124101047  1.3660179977 -1.0312789673 -0.1603831138  1.3808542565
 [157]  0.3981673252  0.0782170137 -0.5004242274 -1.9576898133 -0.1973676692 -0.5107687812
 [163]  0.8885782071 -0.3776975586  0.3029721778  0.1729405738  0.1743314192  0.6277464820
 [169]  0.6926942690 -0.9110840635 -1.9962082423 -0.8877362680 -2.7963706024 -1.1716392497
 [175] -0.0789094089 -1.3171094294  0.1734825676 -0.5191081875  1.0019741503  0.4359270023
 [181] -0.2566651898 -0.2109102523  0.3661902691 -1.4568137588  0.3492782239  1.4454484226
 [187]  0.2984557578  1.4405840450 -0.1366959019 -0.3757449832  0.3884489251  0.1248067015
 [193] -0.9241563484 -0.9509481022 -0.8553846988 -0.6331291826 -0.4475854943  2.3760648396
 [199] -1.0770217389  1.7812185032 -0.6693903252  1.0659910406 -0.0831288983  0.1703141759
 [205]  1.0980525678  0.5469705879 -1.0952901544 -0.2296655268 -0.3141190889 -0.0359529901
 [211] -0.7544255643  0.2081651796 -0.4651465147 -0.4549642529 -0.7519650092 -1.2156701356
 [217] -0.8877739149  1.2765483150  1.6608132896 -1.3875779326 -0.8816676838  1.2223226844
 [223] -1.8888932967  0.7717111179 -1.1083575268 -1.4344274993  0.1384891501  0.2334946326
 [229] -0.7588997598  0.5936897506 -0.4293448555 -0.4901404439  0.3629686638  0.5642335205
 [235]  0.4446171874 -1.0562413362  0.4182887019 -0.3199193184 -1.0258772548  0.2212878488
 [241]  0.5043301895  0.9272561315 -1.5887618053 -0.4562031662 -0.2932108421 -1.2881615705
 [247] -0.4541498923  1.2718397670 -1.1611720909  0.9862936642 -0.3762003418 -0.5825057021
 [253]  1.0384159859  1.3354681175  0.1653864884 -0.7366639423 -0.1955392919  0.1757789978
 [259]  0.3839144117 -0.4709437794 -0.0079258983 -0.0439131891 -0.0425942228 -0.1184529612
 [265]  0.6939558886 -2.0426565928  1.0549262500  0.3795400107  1.3118879306 -1.2208518802
 [271] -0.2621992618 -0.2778650072 -1.8969218197 -0.3968739170 -0.4516884704 -2.8005831834
 [277] -1.3931448432 -0.4833584766 -0.2377747535  0.2600058815 -0.7429721645  0.0482893679
 [283]  0.7495102726 -1.2259738237 -0.2308554482 -0.2774069215 -0.3786835969  0.1729769562
 [289]  0.4840769666  0.5895952850 -0.7303993211 -2.1144922247  0.2665404270  0.5802889247
 [295] -0.4286009707 -0.6245860179  0.4297263781  0.4883468148 -0.0884155602  0.4393558983
 [301] -1.5634611216 -0.3890344403 -0.8404521830 -1.0929108620  0.4609863499 -1.3803245134
 [307]  0.8084930545  0.1504635733 -1.3964516767  0.6690948135  0.5050619527  1.1489508167
 [313]  0.0338057883  0.7290535726 -1.4014423707  0.7630496005 -0.8984142051  0.6420528312
 [319] -0.2282525213  0.3766434599 -2.3516013433 -0.9371435522 -0.4825619250  0.2939587845
 [325]  0.0383559877  1.1439089082  0.0716554772  0.2590486279  0.5871779922 -0.6429144784
 [331]  0.5461801348 -2.1949453251 -2.1439061447  0.6238944917  0.5838495338  1.0875538114
 [337]  0.3542979285  0.2910771281 -1.0603764030  0.3374506873  1.0403939084 -0.1012870815
 [343] -0.8651581035 -1.8252593304  1.4706717457  0.3144398152  1.3584248025  0.9892946825
 [349] -1.1830979521  1.0730210027 -2.1335735849 -0.3975535299  0.4722886848  0.8096082141
 [355] -0.1721915345  0.8969891654 -0.1099673640  0.1449338961  1.1636806663 -0.8537887734
 [361] -0.0563520112 -0.5123573311  1.0122925673  1.0346235063  0.8899433158 -1.2129210488
 [367]  1.2743859541 -1.1008999423  1.3830581401 -1.0157403066  0.8699752220 -0.9852134542
 [373]  0.5511946875  1.0682037290  0.9756738587 -0.3416179619  0.8085618452 -0.2146269653
 [379] -3.0197770306 -1.4399131528  0.4855321541 -0.3089078195  0.7365351930 -1.9657060695
 [385]  0.7052195640 -2.2001662400  0.1444357164 -0.5089762081  0.1308980834  0.2399811543
 [391]  0.9236504113  0.3298646863 -0.0637966610 -0.1046039136  0.0501406061  0.4625328689
 [397] -0.4162252186  1.2292567050 -1.2156005550 -1.2859390980 -0.6627698511 -0.0783005804
 [403] -1.7591298979  1.1460873308  1.0617975572 -2.6455151879 -1.8077383784 -0.2064034562
 [409] -0.3448871154 -0.9440185617 -0.1771719413  1.1617381221  0.1535985559  0.7190970682
 [415]  0.2636624833 -1.4356700339  1.1198082601  0.1414585065  1.3328584313 -1.0039937869
 [421] -0.4850907332  0.2774237132 -1.8310532617 -0.2107635075 -0.1042152043  0.7479471371
 [427]  0.9004157762  1.0721679046  0.1740216952  0.3917611204  1.0028465260  0.3648260781
 [433]  0.9759425017 -1.2996743116 -0.3937165729 -0.1186257389 -1.0837449162  0.3870409361
 [439] -1.2178204104  0.0305965849  1.1589180315 -0.8739061373 -1.1468170625 -0.9828444154
 [445]  0.3030619276  0.2120401187  2.0835948224 -0.9261928838 -0.8555687432 -1.2324028408
 [451] -0.1754164387 -0.3161250995 -0.9812012505  0.0368963344  1.7430093318  0.5362295586
 [457] -0.8231598235  0.5739436924 -0.1338653928 -0.2178264225  1.0599310361 -1.8300896942
 [463]  0.0758098038  0.1013561368  0.8799397303  1.0820808152  0.6695828422 -0.1830047431
 [469]  0.9511991304 -0.4022301529  1.9834432727 -1.2170693461  1.0915293080  1.3486839110
 [475] -0.0056505346  1.1531873883  0.3941395460 -0.3086368639  2.6399820132 -0.7781042581
 [481] -0.0830970765 -0.7113610066  1.7383407613 -0.8281340530 -0.4326850868 -0.0082626593
 [487]  0.3392950577 -0.9343191382 -0.3699102681  0.7449992109 -1.7182590647 -0.0022251516
 [493] -1.0323792107 -0.6849046094 -0.9983749057 -1.8675399871  1.3494773740  1.2692548361
 [499] -1.5458307173 -1.4229315127  0.6150833543 -0.1701199304 -0.1262114087 -1.8207329207
 [505]  0.6072275129  1.3631370612 -0.1720523705 -1.3354236024  0.5794132049 -1.0370650661
 [511] -0.1964095369 -0.6630903264  0.3537471848 -0.0202464377  0.4085387640 -0.6604497657
 [517]  0.8528258313 -1.5226723085 -1.2047227396  0.8481179138 -1.1011669134  0.0736077801
 [523] -0.7887444017 -0.8422621729  0.2679104685  0.0387766046  1.3971969198  0.0894594966
 [529] -0.8539455847  0.5807202237  0.2474115750 -0.6134467019  0.5036008304 -0.8069318422
 [535] -0.6152181762 -0.7972712884 -0.3230041521 -1.3820742553  1.1280968648 -0.4797628709
 [541]  0.2151497798 -0.1148639103 -1.2890733191  0.1322253611  0.6071687730  1.1136250300
 [547] -0.4945031312  0.7031365383  0.3737429791  1.8337307473 -0.0001604709  1.2662036779
 [553]  0.5754874386  0.0283142153 -0.2338235108  0.7097683244 -0.4043428070  1.9850236508
 [559]  1.0992245225  0.6147189716 -0.3240318320  2.0641215271 -0.7729958723 -0.9481739793
 [565] -0.7215398954  0.7836308807  0.1328115927  2.3423715427  0.3628261578 -0.2521870076
 [571] -0.9303081113 -1.8693407170 -0.2535560361  1.2561556569  0.0059642547  1.0612964645
 [577]  0.9258952621  1.4174644989  0.8092450232 -0.6313943546  0.6842509923  1.5272899306
 [583]  0.4483869448 -0.4807254253 -1.8912535222  0.8272027590 -0.7355453463  1.2578035235
 [589] -0.5851725983 -0.7265521833 -0.6291112211 -1.2112565283  2.0529098580  0.4657295383
 [595]  0.5183574712 -0.0315992237 -3.2880794969 -0.5071015429  1.1196635310  0.5644864869
 [601] -0.4461471501 -0.2141904412 -0.5108468396 -0.5784043631 -1.1199697801  0.6240692237
 [607]  2.3742370170  0.3021269285  0.2173664554 -1.1257663701  0.5922534705  0.4900415452
 [613]  1.3466291653  0.7696063735 -1.3646886580  0.2209433426 -0.5948590024  0.2658913289
 [619] -0.3854789335  0.9662862161 -0.6433029636  1.2979385526 -1.0289911007 -0.4962904315
 [625]  0.1180701424 -0.3024635968  0.2552250181  0.1049974979  0.3216572088  0.3390130277
 [631] -0.6825539039  0.0861184217  0.2649676880  0.6597165224 -1.3223429180  1.5072753560
 [637] -0.5839440596  0.7501329985  0.5207796151 -0.3815056019  0.4038825879  0.3348182621
 [643] -1.0475878373 -0.6267172914 -1.8316886830 -0.4581268169  0.0980500565 -0.3632028667
 [649] -0.2819481962 -0.8917918206 -0.1398340827  1.1750072154 -0.4222730978 -1.1986207456
 [655]  0.9749167576 -1.5774085098 -1.2820731660 -0.3842849206  1.6986009117 -0.6695021162
 [661]  1.5480040606  0.3202436113 -0.4971358675 -0.3361878475  0.2621077424 -0.1001836924
 [667] -2.6649080316 -0.4423952514  0.6288091804  2.2879179774 -0.0024740558  0.1374803812
 [673] -1.3827109925 -2.2744270473  1.4590793224 -0.0489789880 -0.0024583227  0.1966406610
 [679] -0.4497505749  0.6091902218  1.3311370356 -1.0779620658  0.2140038735  1.3454601093
 [685] -1.8820448771 -0.7455887376  0.7718415091 -1.0417078358  0.0922514950 -0.6233922959
 [691] -0.1637544747 -0.9360395236 -0.1057292626 -0.7474807156 -0.9482341613 -2.1435586687
 [697]  0.1311866518 -1.9054325177  0.7317472004 -0.1237830775 -0.8841177149  0.1619400936
 [703] -0.6678236722 -0.9430193039 -1.0006684941  0.8974972638  0.3821926241  1.1177542168
 [709]  1.4290094819 -0.5381161200 -0.6174932417  0.6325497710  1.5809340901 -0.5110466859
 [715]  1.6860393001 -2.1578932476  0.8602418294 -1.6019974432 -1.2949308499 -0.9105834441
 [721] -0.4036346906 -0.6498674494 -0.7080907120  1.1107625966  0.6894047119 -0.0473047157
 [727] -1.5359363148  1.4277859951  0.8875965682 -1.0242322178 -0.1635233247  0.0399697253
 [733] -0.4584196231  0.4542733025  0.2732186547  0.3816086879 -0.0369225892  0.7643009896
 [739]  0.5287765963  0.1702434905  0.5647060415 -0.0960903911  1.4569345377  0.6772789424
 [745]  1.7253500040  1.1490670195 -2.3961129696  0.8638837146 -1.8870866056 -0.6131840090
 [751] -1.5521785063  0.2237066961 -0.4821454401 -0.5013908156 -1.8711299111  0.0609932709
 [757]  0.9437077455  1.9675905631 -0.0844874789  0.0452592213 -0.2538408550  1.6521585458
 [763] -0.7588375443  1.8452372664 -0.2629346449 -0.0553913765 -0.5902212984 -0.6019937202
 [769] -0.8651553619  1.2600660903  0.7098091908 -0.2297164055 -1.1122138339  1.5661949071
 [775] -1.6845109703 -1.4752951074 -0.5298559947 -0.3179555686 -0.2597190355 -0.6933954067
 [781] -1.6992949112 -1.0984561598  0.1982556713 -0.3577899539  1.1828040041 -0.9411332182
 [787]  0.7456735599 -0.4092785459 -1.2568425663 -1.4099194282  0.4375458400 -0.4815969585
 [793] -1.4083818631 -0.8853969614 -0.4867863790 -0.3667618991 -0.2056603821  2.0428769157
 [799] -1.4254627893 -1.2814635234  0.9226410057 -0.3375440249 -1.2240785957 -0.6136596150
 [805] -0.4433954674  0.4669085711 -1.4839655767 -0.2058600459 -0.0641612756 -0.6807967465
 [811] -0.6434819402 -1.0165991850  1.3082362157 -0.1566031407 -0.6762647977  0.8789587552
 [817]  0.2929330863 -0.3478269781 -0.0417413555 -0.7808306212  0.0900804010 -0.9972722545
 [823]  0.5066750802  0.6094109449 -1.7931361330 -1.2060623602  2.0733857473  0.6100693664
 [829] -0.4657449500 -0.0268072764  0.4726144489 -1.0789855626  0.8689924469  0.1356698253
 [835]  0.9031154724  0.3413049612 -0.9456094591  0.0722031916 -1.6772517274  1.1702777116
 [841]  0.7765122033  0.4371390842  0.3463441447  0.5520424433 -0.3062450602  1.8774739688
 [847] -0.5181500704 -1.4423294692  0.4694128531 -1.2042872109  1.0895492206  0.6865261290
 [853]  0.9410823453  0.1048516406  0.2822939324 -1.3804257527  1.2566072942  0.1985974382
 [859]  0.0173603441  0.6538897893  0.2789581462  0.5652335104 -0.2914935631 -1.3558271913
 [865] -1.4467035880  0.2446585372 -0.8089915225 -0.2408512877  0.5910908569  0.7841104229
 [871] -1.0439824286 -0.7950285160  1.0763663520  0.0057347239  1.0355604292  1.0873527846
 [877]  0.7159414599 -0.4529372698  0.0846149815 -0.8635300978  0.6774346594 -0.5145342546
 [883] -0.2108001000  0.1623719284 -0.2514485865 -0.8805777050 -1.3010464786 -0.0903324003
 [889] -0.5829701992  0.6375131606 -0.6565357892 -1.2210926437 -0.0206487349  1.7317231241
 [895]  2.0223207133  0.3986751963 -0.4816823146 -0.7019355694 -0.1290522611 -0.2544156516
 [901] -0.0500307115  1.6234462115  0.6354589384  0.2427297530  0.6244173210 -0.2728068227
 [907] -0.7867846255  1.2744210145 -1.4390712782 -0.5805451252 -0.3620196672 -0.8739617419
 [913]  0.1420994929 -0.3127030309 -0.3278364613 -1.9888211222  0.0319505880 -0.0294329028
 [919] -0.4788029532  0.0232925632 -0.4491929978  1.3193176133  0.7915432239  0.2973126285
 [925] -1.4423402736 -0.4699227527 -1.4015419748 -1.3579048747 -0.0416514732 -0.2873345025
 [931] -1.7548840266  0.2465327657  0.9647120161 -1.4723106802 -1.7971940307 -0.7212587920
 [937] -0.5324757880 -1.6681320734 -1.0903127981 -1.0794577310  0.2393104211  0.1710187895
 [943]  1.0466269540 -1.1765894039  1.1727333847 -0.2729295202  2.3337115517 -1.1911545391
 [949]  0.5681989119 -0.0192888478 -0.4030925516  1.5595668092 -0.8631461678 -0.3073035742
 [955]  0.1749001924 -0.3584617943 -0.2610739804  0.1854753952 -1.1155087764  0.1992582496
 [961] -0.3064146088 -0.2004048420  2.0867127216  0.7541837578  0.6333939983  0.6418079999
 [967]  0.3628946988  0.5698125447 -0.3813005343 -0.7811857953 -0.0182120764 -1.0500058822
 [973]  0.6043748833 -1.7358173640  0.4250473298 -1.1251386817 -0.9100072036  0.0588459735
 [979]  0.5313445468  0.4826424253 -0.3411557915 -1.0852044962  1.2117070463 -0.8527936341
 [985]  0.4557036073  1.0432227352  1.8704508832 -0.4975400904  2.0484354920 -0.5851289005
 [991]  1.5856592290  0.5571863580  1.1604356443 -1.5584210871 -1.0036831126 -0.1991166654
 [997] -0.1733097057 -0.1375692530 -0.4046235027  0.6022858768
 [ reached getOption("max.print") -- omitted 11897 entries ]

$rawpval
   [1] 0.548267478 0.140598004 0.501541731 0.190358133 0.751954096 0.978721864 0.795342688
   [8] 0.941466129 0.776728649 0.297175893 0.254094400 0.108366738 0.799731001 0.787289273
  [15] 0.859097487 0.700399935 0.841444443 0.600758734 0.771732986 0.144320673 0.743757885
  [22] 0.734352724 0.976337356 0.982235968 0.625268036 0.074820928 0.255875519 0.537691147
  [29] 0.326507803 0.217578895 0.138671970 0.179821099 0.612847189 0.002963336 0.637916178
  [36] 0.977194877 0.990430782 0.639287981 0.878161653 0.963586852 0.631076967 0.020640183
  [43] 0.214981762 0.221124287 0.086307232 0.378203449 0.948889000 0.740777561 0.867767414
  [50] 0.857930979 0.620394763 0.252851530 0.576184588 0.892763399 0.754807092 0.996682504
  [57] 0.427253301 0.117773659 0.051038469 0.383691513 0.671293542 0.894645089 0.850044439
  [64] 0.352164499 0.432650201 0.498387985 0.942161690 0.278684472 0.296110472 0.606804990
  [71] 0.018992717 0.385078707 0.933443866 0.853267216 0.705611446 0.628858066 0.486090234
  [78] 0.292000596 0.569634794 0.466138804 0.875305400 0.435180091 0.048467327 0.236685519
  [85] 0.909217711 0.591704128 0.890409681 0.636119506 0.499525528 0.063053271 0.977383397
  [92] 0.223961850 0.280052463 0.160240520 0.634416773 0.726232827 0.747547680 0.993984133
  [99] 0.653723345 0.160493410 0.452037631 0.803725003 0.035019064 0.976371260 0.462580365
 [106] 0.184845723 0.504031567 0.703311086 0.720040348 0.575350898 0.223290322 0.026304559
 [113] 0.743264951 0.786141429 0.623927992 0.252248249 0.219636886 0.071737548 0.134247342
 [120] 0.934666399 0.184093811 0.799121755 0.562216450 0.808266081 0.901583176 0.329790026
 [127] 0.257216839 0.604495240 0.916193287 0.596750439 0.657073000 0.565045446 0.901623139
 [134] 0.230726585 0.800629312 0.459643563 0.128107673 0.025214060 0.441078886 0.902587392
 [141] 0.704714751 0.629518647 0.588508241 0.908185877 0.839101033 0.011474809 0.690449048
 [148] 0.344122284 0.594338814 0.082061961 0.213484922 0.504950788 0.085966657 0.848794991
 [155] 0.563710355 0.083661889 0.345253425 0.468827714 0.691611801 0.974866789 0.578230086
 [162] 0.695243514 0.187114904 0.647172359 0.380955532 0.431349069 0.430802504 0.265085015
 [169] 0.244250718 0.818874459 0.977044370 0.812658684 0.997415995 0.879329039 0.531447660
 [176] 0.906099027 0.431136065 0.698157350 0.158178039 0.331444847 0.601281377 0.583521348
 [183] 0.357111551 0.927416102 0.363440223 0.074165985 0.382677669 0.074851115 0.554364416
 [190] 0.646446754 0.348841922 0.450338291 0.822297540 0.829184639 0.803830894 0.736675357
 [197] 0.672773813 0.008749194 0.859264723 0.037438381 0.748376739 0.143213846 0.533125476
 [204] 0.432381532 0.136090768 0.292199469 0.863305230 0.590824158 0.623284704 0.514340078
 [211] 0.774703137 0.417549998 0.679086731 0.675432522 0.773963949 0.887944698 0.812668811
 [218] 0.100880879 0.048375476 0.917367198 0.811021729 0.110792812 0.970546936 0.220142772
 [225] 0.866146280 0.924274848 0.444926922 0.407688668 0.776043739 0.276359820 0.666163861
 [232] 0.687982740 0.358314146 0.286297609 0.328298212 0.854571015 0.337868025 0.625485254
 [239] 0.847525277 0.412434155 0.307014683 0.176896783 0.943942910 0.675878056 0.615319501
 [246] 0.901155137 0.675139527 0.101715028 0.877214039 0.161994514 0.646616019 0.719886949
 [253] 0.149538215 0.090861592 0.434319894 0.769336630 0.577514610 0.430233785 0.350520942
 [260] 0.681159558 0.503161943 0.517513199 0.516987500 0.547145618 0.243854936 0.979456778
 [267] 0.145729528 0.352143449 0.094778975 0.888928947 0.603416089 0.609442007 0.971080871
 [274] 0.654269780 0.674253287 0.997449482 0.918212002 0.685579387 0.593972100 0.397429618
 [281] 0.771250733 0.480742814 0.226774855 0.889895737 0.591286448 0.609266166 0.647538582
 [288] 0.431334770 0.314165624 0.277731007 0.767426933 0.982763376 0.394911514 0.280859897
 [295] 0.665893181 0.733878601 0.333697346 0.312652103 0.535226803 0.330201838 0.941027913
 [302] 0.651374665 0.799672549 0.862783531 0.322404199 0.916256625 0.209403401 0.440199444
 [309] 0.918710739 0.251717499 0.306757662 0.125288131 0.486516010 0.232984446 0.919459085
 [316] 0.222716908 0.815517617 0.260419440 0.590275034 0.353219293 0.990653602 0.825657639
 [323] 0.685296590 0.384394699 0.484701926 0.126330708 0.471438045 0.397798861 0.278542086
 [330] 0.739860203 0.292471060 0.985916240 0.983979796 0.266348439 0.279660774 0.138396065
 [337] 0.361557810 0.385496166 0.855513304 0.367888590 0.149078465 0.540338715 0.806523991
 [344] 0.966019048 0.070689955 0.376593510 0.087164464 0.161259492 0.881614835 0.141630846
 [351] 0.983561149 0.654520340 0.318360373 0.209082693 0.568356524 0.184862349 0.543782371
 [358] 0.442381532 0.122276724 0.803388982 0.522469307 0.695799526 0.155699094 0.150422386
 [365] 0.186748162 0.887419996 0.101263334 0.864529896 0.083323520 0.845123466 0.192156973
 [372] 0.837740383 0.290750111 0.142714312 0.164613055 0.633680791 0.209383609 0.584970916
 [379] 0.998735196 0.925054014 0.313649455 0.621304176 0.230702529 0.975333713 0.240336804
 [386] 0.986102449 0.442578207 0.694615550 0.447927965 0.405172433 0.177834179 0.370751104
 [393] 0.525433932 0.541654945 0.480005171 0.321849619 0.661377396 0.109487787 0.887931439
 [400] 0.900767841 0.746261017 0.531205522 0.960722276 0.125879510 0.144163800 0.995921669
 [407] 0.964676386 0.581762114 0.634910381 0.827419918 0.570313333 0.122670928 0.438963138
 [414] 0.236040557 0.396020003 0.924451872 0.131397739 0.443753869 0.091289133 0.842309196
 [421] 0.686194007 0.390727388 0.966453703 0.583464092 0.541500716 0.227246022 0.183949514
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 [ reached getOption("max.print") -- omitted 11897 entries ]

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 [919] 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233
 [928] 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233
 [937] 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233
 [946] 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233
 [955] 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233
 [964] 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233
 [973] 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233
 [982] 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233
 [991] 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233 0.9999233
[1000] 0.9999233
 [ reached getOption("max.print") -- omitted 11897 entries ]

hist(invnormcomb$rawpval, breaks=100, col="grey", main="Inverse normal method", xlab = "Raw p-values Interaction (meta-analysis)")

## Summarize the results. DE are the results from the original anlayses of the datasets individually.
I_results_metastats <- data.frame(DE, "DE.fishercomb_adjP"=fishcomb$adjpval, "DE.invnorm_adj_P"=invnormcomb$adjpval)
library(data.table)
setDT(I_results_metastats, keep.rownames = "Gene")  # make the rownames a part of the dataframe with column name "Gene"
head(I_results_metastats)

I_results <- data.frame(DE, "DE.fishercomb"=ifelse(fishcomb$adjpval<=0.05,1,0), "DE.invnorm"=ifelse(invnormcomb$adjpval<=0.05,1,0))
head(I_results)

## Dealing with conflicts in the sign of differential expression 
signsFC <- mapply(I_FC, FUN=function(x) sign(x))
sumsigns <- apply(signsFC,1,sum)
commonsngFC <- ifelse(abs(sumsigns)==dim(signsFC)[2], sign(sumsigns), 0)
unionDE <- unique(c(fishcomb$DEindices, invnormcomb$DEindicies))
FC.selecDE <- data.frame(I_results[unionDE,], do.call(cbind,I_FC) [unionDE,], signFC=commonsngFC[unionDE], DE=DE[unionDE])
  # keep the ones that don't have a conflict (1 or -1)
keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),]
conflictDE <- FC.selecDE[which(FC.selecDE$signFC==0),]
head(FC.selecDE)  # the ones with conflict = 0

head(keepDE)

head(conflictDE)

table(conflictDE$DE)

< table of extent 0 >

### Merge results and write to  a file
setDT(FC.selecDE, keep.rownames = "Gene")
head(FC.selecDE)

head(I_results_metastats)

# merge top.tables from HS2008 and 2012 - all genes that were not originally filtered otu.
dim(top.table_I_2008)

[1] 13067     7

dim(top.table_I_2012)

[1] 14431     7

top.table.I_All<- merge(top.table_I_2008,top.table_I_2012,by="Gene",all.x = TRUE, all.y=TRUE, suffixes = c(".08", ".12"))
dim(top.table.I_All)

[1] 14601    13

# merge with metanalysis
Interaction_results <- merge(top.table.I_All,FC.selecDE, all.x = TRUE)
Interaction_results2 <- merge(Interaction_results, I_results_metastats, all.x = TRUE)
head(Interaction_results2)

write.table(Interaction_results2, file = "OutputFiles/Meta_Interection.txt", row.names = F, sep = "\t", quote = F)

Make summary tables

Interaction between treatment and ecotype

## Make a table of results
fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)]
invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)]
indstudy_de <- list(rownames(keepDE)[which(keepDE[,"DE.2008Dataset"]==1)],rownames(keepDE)[which(keepDE[,"DE.2012Dataset"]==1)])
IDD.IRR(fishcomb_de,indstudy_de)

  DE  IDD Loss  IDR  IRR 
   0    0    0  NaN  NaN 

IDD.IRR(invnorm_de, indstudy_de)

  DE  IDD Loss  IDR  IRR 
   0    0    0  NaN  NaN 

Make UpsetPlot Interaction

# Interaction 2008
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_I_08_top <- top.table_I_2008[which(top.table_I_2008$adj.P.Val<=0.05), ]
dim(UP_I_08_top)

[1] 0 7

  # converting column of "Gene" in dataframe column into vector by passing as index
Interaction_HS2008 = UP_I_08_top[['Gene']]
# Interaction 2012
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_I_12_top <- top.table_I_2012[which(top.table_I_2012$adj.P.Val<=0.05), ]
dim(UP_I_12_top)

[1] 0 7

  #converting column of "Gene" in dataframe column into vector by passing as index
Interaction_HS2012 = UP_I_12_top[['Gene']]
# Heat MetaAnalysis
## Keep the genes from the meta-analysis that have invernorm adjusted p-value <= 0.05 and the log fold change is going in the same direction in both analyses
UP_MetaA <- Interaction_results2[which(Interaction_results2$DE.invnorm_adj_P<=0.05), ]
UP_MetaB <- UP_MetaA[which(abs(UP_MetaA$signFC)==1),]
dim(UP_MetaB)

[1]  0 23

  #converting column of "Gene" in dataframe column into vector by passing as index
Interaction_Meta = UP_MetaB[['Gene']]

listInteraction <- list(HS2008 = Interaction_HS2008, HS2012 = Interaction_HS2012, MetaAnalysis = Interaction_Meta)

### No significant genes for the Interaction so don't need to make upset plot
# Make upset put and save as a .pdf file
#pdf("OutputGraphs/Upset_Interaction.pdf")
  #make plot
#upset(fromList(listInteraction), mainbar.y.label = "Number of Genes Differentially expressed genes (adjusted p-value=0.05)", sets.x.label = "DEGs",order.by = "freq")
#dev.off()

## Ecotype in Control Meta-Analysis
first get the data ready

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI01lcmdlIHRvcCB0YWJsZXMgcmVzdWx0cyBmb3IgZWFjaCBkYXRhc2V0IC0ga2VlcCB0aGUgZ2VuZXMgaW4gY29tbW9uIH4xMzAwMFxubGlicmFyeShwbHlyKVxudG9wLnRhYmxlX0VDX2ludGVyc2VjdCA8LSBtZXJnZSh0b3AudGFibGVfQ19FY290eXBlczA4LHRvcC50YWJsZV9DX0Vjb3R5cGVzMTIsYnk9XCJHZW5lXCIsYWxsLnggPSBGQUxTRSwgc3VmZml4ZXMgPSBjKFwiLjA4XCIsIFwiLjEyXCIpKVxuZGltKHRvcC50YWJsZV9FQ19pbnRlcnNlY3QpXG5gYGAifQ== -->

```r
#Merge top tables results for each dataset - keep the genes in common ~13000
library(plyr)
top.table_EC_intersect <- merge(top.table_C_Ecotypes08,top.table_C_Ecotypes12,by="Gene",all.x = FALSE, suffixes = c(".08", ".12"))
dim(top.table_EC_intersect)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEyODk3ICAgIDEzXG4ifQ== -->

```
[1] 12897    13
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3VtbWFyeSh0b3AudGFibGVfRUNfaW50ZXJzZWN0KVxuYGBgIn0= -->

```r
summary(top.table_EC_intersect)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin 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 -->

```
     Gene              logFC.08         AveExpr.08          t.08           P.Value.08       
 Length:12897       Min.   :-6.8432   Min.   :-2.530   Min.   :-6.8659   Min.   :0.0000097  
 Class :character   1st Qu.:-0.8377   1st Qu.: 1.378   1st Qu.:-1.0837   1st Qu.:0.2130375  
 Mode  :character   Median :-0.2058   Median : 3.343   Median :-0.3739   Median :0.4492140  
                    Mean   :-0.3905   Mean   : 3.261   Mean   :-0.3411   Mean   :0.4676632  
                    3rd Qu.: 0.1886   3rd Qu.: 4.970   3rd Qu.: 0.3751   3rd Qu.:0.7137507  
                    Max.   : 7.0517   Max.   :15.915   Max.   : 5.1647   Max.   :0.9999763  
  adj.P.Val.08         B.08            logFC.12            AveExpr.12          t.12          
 Min.   :0.1262   Min.   :-5.9354   Min.   :-10.041843   Min.   :-5.122   Min.   :-4.274807  
 1st Qu.:0.8463   1st Qu.:-5.4882   1st Qu.: -0.171177   1st Qu.: 1.965   1st Qu.:-0.673439  
 Median :0.8968   Median :-5.0479   Median :  0.001025   Median : 3.609   Median : 0.004213  
 Mean   :0.9004   Mean   :-4.9933   Mean   : -0.015221   Mean   : 3.543   Mean   : 0.009372  
 3rd Qu.:0.9507   3rd Qu.:-4.6579   3rd Qu.:  0.161458   3rd Qu.: 5.057   3rd Qu.: 0.686086  
 Max.   :1.0000   Max.   :-0.2773   Max.   :  6.393448   Max.   :16.730   Max.   : 5.658055  
   P.Value.12         adj.P.Val.12         B.12       
 Min.   :0.0000181   Min.   :0.1476   Min.   :-4.760  
 1st Qu.:0.2650969   1st Qu.:0.9871   1st Qu.:-4.702  
 Median :0.5040173   Median :0.9871   Median :-4.643  
 Mean   :0.5052276   Mean   :0.9886   Mean   :-4.617  
 3rd Qu.:0.7452445   3rd Qu.:0.9909   3rd Qu.:-4.582  
 Max.   :0.9998436   Max.   :0.9998   Max.   :-2.893  
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZCh0b3AudGFibGVfRUNfaW50ZXJzZWN0KVxuYGBgIn0= -->

```r
head(top.table_EC_intersect)
```

<!-- rnb-source-end -->

<!-- rnb-frame-begin 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 -->

<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Gene"],"name":[1],"type":["chr"],"align":["left"]},{"label":["logFC.08"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["AveExpr.08"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["t.08"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["P.Value.08"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["adj.P.Val.08"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["B.08"],"name":[7],"type":["dbl"],"align":["right"]},{"label":["logFC.12"],"name":[8],"type":["dbl"],"align":["right"]},{"label":["AveExpr.12"],"name":[9],"type":["dbl"],"align":["right"]},{"label":["t.12"],"name":[10],"type":["dbl"],"align":["right"]},{"label":["P.Value.12"],"name":[11],"type":["dbl"],"align":["right"]},{"label":["adj.P.Val.12"],"name":[12],"type":["dbl"],"align":["right"]},{"label":["B.12"],"name":[13],"type":["dbl"],"align":["right"]}],"data":[{"1":"A1CF","2":"0.6456137","3":"6.907570","4":"2.2544293","5":"0.04146675","6":"0.8329518","7":"-3.800067","8":"-0.07363706","9":"7.083357","10":"-0.4497062","11":"0.6579694","12":"0.9870699","13":"-4.721219","_rn_":"1"},{"1":"AAAS","2":"0.3749279","3":"1.997824","4":"0.6641807","5":"0.51782259","6":"0.9095458","7":"-5.092783","8":"0.40645009","9":"1.900927","10":"1.4731112","11":"0.1569627","12":"0.9870699","13":"-4.522374","_rn_":"2"},{"1":"AACS","2":"-1.5717239","3":"1.485685","4":"-1.2030509","5":"0.24974865","6":"0.8463084","7":"-4.593940","8":"-0.18002254","9":"1.825707","10":"-0.5017408","11":"0.6215674","12":"0.9870699","13":"-4.643616","_rn_":"3"},{"1":"AADAT","2":"0.3691794","3":"4.079697","4":"0.6446844","5":"0.52999676","6":"0.9114417","7":"-5.533232","8":"-0.12998793","9":"4.845994","10":"-0.8852574","11":"0.3869950","12":"0.9870699","13":"-4.646532","_rn_":"4"},{"1":"AAGAB","2":"0.4512019","3":"5.983880","4":"1.2408975","5":"0.23589552","6":"0.8463084","7":"-5.169551","8":"0.04779684","9":"6.234108","10":"0.2666810","11":"0.7925643","12":"0.9910001","13":"-4.745871","_rn_":"5"},{"1":"AAK1","2":"0.1299334","3":"5.952346","4":"0.5482333","5":"0.59253365","6":"0.9232788","7":"-5.730231","8":"0.06362297","9":"5.027270","10":"0.3479984","11":"0.7316362","12":"0.9907331","13":"-4.740324","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10],"total":[13]},"rows":{"min":[10],"max":[10],"total":[6]},"pages":{}}}
  </script>
</div>

<!-- rnb-frame-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG4jUHV0IHAtdmFsdWUgYW5kIEZvbGQgY2hhbmdlcyByZXN1bHRzIGluIGxpc3RzXG5FQ19yYXdwdmFsIDwtIGxpc3QoXCJwdmFsdWVfMDhcIj10b3AudGFibGVfRUNfaW50ZXJzZWN0W1tcIlAuVmFsdWUuMDhcIl1dLCBcInB2YWx1ZV8xMlwiPXRvcC50YWJsZV9FQ19pbnRlcnNlY3RbW1wiUC5WYWx1ZS4xMlwiXV0pXG5zdW1tYXJ5KEVDX3Jhd3B2YWwpXG5gYGAifQ== -->

```r

#Put p-value and Fold changes results in lists
EC_rawpval <- list("pvalue_08"=top.table_EC_intersect[["P.Value.08"]], "pvalue_12"=top.table_EC_intersect[["P.Value.12"]])
summary(EC_rawpval)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgIExlbmd0aCBDbGFzcyAgTW9kZSAgIFxucHZhbHVlXzA4IDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucHZhbHVlXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->

```
          Length Class  Mode   
pvalue_08 12897  -none- numeric
pvalue_12 12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRUNfYWRqcHZhbCA8LSBsaXN0KFwiYWRqcHZhbHVlXzA4XCI9dG9wLnRhYmxlX0VDX2ludGVyc2VjdFtbXCJhZGouUC5WYWwuMDhcIl1dLCBcImFkanB2YWx1ZV8xMlwiPXRvcC50YWJsZV9FQ19pbnRlcnNlY3RbW1wiYWRqLlAuVmFsLjEyXCJdXSlcbnN1bW1hcnkoRUNfYWRqcHZhbClcbmBgYCJ9 -->

```r
EC_adjpval <- list("adjpvalue_08"=top.table_EC_intersect[["adj.P.Val.08"]], "adjpvalue_12"=top.table_EC_intersect[["adj.P.Val.12"]])
summary(EC_adjpval)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgIExlbmd0aCBDbGFzcyAgTW9kZSAgIFxuYWRqcHZhbHVlXzA4IDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuYWRqcHZhbHVlXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->

```
             Length Class  Mode   
adjpvalue_08 12897  -none- numeric
adjpvalue_12 12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuRUNfRkMgPC0gbGlzdChcIkVfRkNfMDhcIj10b3AudGFibGVfRUNfaW50ZXJzZWN0W1tcImxvZ0ZDLjA4XCJdXSwgXCJFX0ZDXzEyXCI9dG9wLnRhYmxlX0VDX2ludGVyc2VjdFtbXCJsb2dGQy4xMlwiXV0pXG5zdW1tYXJ5KEVDX0ZDKVxuYGBgIn0= -->

```r
EC_FC <- list("E_FC_08"=top.table_EC_intersect[["logFC.08"]], "E_FC_12"=top.table_EC_intersect[["logFC.12"]])
summary(EC_FC)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkVfRkNfMDggMTI4OTcgIC1ub25lLSBudW1lcmljXG5FX0ZDXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->

```
        Length Class  Mode   
E_FC_08 12897  -none- numeric
E_FC_12 12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI21ha2UgbWF0cml4IG9mIDEgZm9yIGdlbmVzIERFIGFuZCAwIGZvciBub3QuXG5ERTwtIG1hcHBseShFQ19hZGpwdmFsLCBGVU49ZnVuY3Rpb24oeCkgaWZlbHNlKHggPD0gMC4wNSwgMSwgMCkpXG5zdHVkaWVzIDwtYyhcIjIwMDhEYXRhc2V0XCIsIFwiMjAxMkRhdGFzZXRcIilcbmNvbG5hbWVzKERFKT1wYXN0ZShcIkRFXCIsIHN0dWRpZXMsc2VwPVwiLlwiKVxucm93bmFtZXMoREUpPXBhc3RlKHRvcC50YWJsZV9FQ19pbnRlcnNlY3QkXCJHZW5lXCIpXG4jREU8LWNiaW5kKHRvcC50YWJsZV9IX2ludGVyc2VjdCRcIkdlbmVcIiwgREUpXG5zdW1tYXJ5KERFKVxuYGBgIn0= -->

```r
#make matrix of 1 for genes DE and 0 for not.
DE<- mapply(EC_adjpval, FUN=function(x) ifelse(x <= 0.05, 1, 0))
studies <-c("2008Dataset", "2012Dataset")
colnames(DE)=paste("DE", studies,sep=".")
rownames(DE)=paste(top.table_EC_intersect$"Gene")
#DE<-cbind(top.table_H_intersect$"Gene", DE)
summary(DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiIERFLjIwMDhEYXRhc2V0IERFLjIwMTJEYXRhc2V0XG4gTWluLiAgIDowICAgICAgTWluLiAgIDowICAgICBcbiAxc3QgUXUuOjAgICAgICAxc3QgUXUuOjAgICAgIFxuIE1lZGlhbiA6MCAgICAgIE1lZGlhbiA6MCAgICAgXG4gTWVhbiAgIDowICAgICAgTWVhbiAgIDowICAgICBcbiAzcmQgUXUuOjAgICAgICAzcmQgUXUuOjAgICAgIFxuIE1heC4gICA6MCAgICAgIE1heC4gICA6MCAgICAgXG4ifQ== -->

```
 DE.2008Dataset DE.2012Dataset
 Min.   :0      Min.   :0     
 1st Qu.:0      1st Qu.:0     
 Median :0      Median :0     
 Mean   :0      Mean   :0     
 3rd Qu.:0      3rd Qu.:0     
 Max.   :0      Max.   :0     
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChERSlcbmBgYCJ9 -->

```r
head(DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgREUuMjAwOERhdGFzZXQgREUuMjAxMkRhdGFzZXRcbkExQ0YgICAgICAgICAgICAgICAwICAgICAgICAgICAgICAwXG5BQUFTICAgICAgICAgICAgICAgMCAgICAgICAgICAgICAgMFxuQUFDUyAgICAgICAgICAgICAgIDAgICAgICAgICAgICAgIDBcbkFBREFUICAgICAgICAgICAgICAwICAgICAgICAgICAgICAwXG5BQUdBQiAgICAgICAgICAgICAgMCAgICAgICAgICAgICAgMFxuQUFLMSAgICAgICAgICAgICAgIDAgICAgICAgICAgICAgIDBcbiJ9 -->

```
      DE.2008Dataset DE.2012Dataset
A1CF               0              0
AAAS               0              0
AACS               0              0
AADAT              0              0
AAGAB              0              0
AAK1               0              0
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGltKERFKVxuYGBgIn0= -->

```r
dim(DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEyODk3ICAgICAyXG4ifQ== -->

```
[1] 12897     2
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI0NoZWNrIGZvciBub3JtYWwgZGlzdHJpYnV0aW9uc1xucGFyKG1mcm93ID0gYygxLDIpKVxuaGlzdChFQ19yYXdwdmFsW1sxXV0sIGJyZWFrcz0xMDAsIGNvbD1cImdyZXlcIiwgbWFpbj1cIjIwMDggRGF0YXNldFwiLCB4bGFiPVwiUmF3IHAtdmFsdWVzXCIpXG5oaXN0KEVDX3Jhd3B2YWxbWzJdXSwgYnJlYWtzPTEwMCwgY29sPVwiZ3JleVwiLCBtYWluPVwiMjAxMiBEYXRhc2V0XCIsIHhsYWI9XCJSYXcgcC12YWx1ZXNcIilcblxuYGBgIn0= -->

```r
#Check for normal distributions
par(mfrow = c(1,2))
hist(EC_rawpval[[1]], breaks=100, col="grey", main="2008 Dataset", xlab="Raw p-values")
hist(EC_rawpval[[2]], breaks=100, col="grey", main="2012 Dataset", xlab="Raw p-values")

```

<!-- rnb-source-end -->

<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

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" />

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### Calculate the meta-analysis p-value 
Ecotype in Control

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShtZXRhUk5BU2VxKVxuIyBQLXZhbHVlIGNvbWJpbmF0aW9uIHVzaW5nIEZpc2hlciBtZXRob2QgXG5maXNoY29tYiA8LSBmaXNoZXJjb21iKEVDX3Jhd3B2YWwsIEJIdGggPSAwLjA1KVxuc3VtbWFyeShmaXNoY29tYilcbmBgYCJ9 -->

```r
library(metaRNASeq)
# P-value combination using Fisher method 
fishcomb <- fishercomb(EC_rawpval, BHth = 0.05)
summary(fishcomb)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkRFaW5kaWNlcyAgICAgICAgIDAgIC1ub25lLSBudW1lcmljXG5UZXN0U3RhdGlzdGljIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucmF3cHZhbCAgICAgICAxMjg5NyAgLW5vbmUtIG51bWVyaWNcbmFkanB2YWwgICAgICAgMTI4OTcgIC1ub25lLSBudW1lcmljXG4ifQ== -->

```
              Length Class  Mode   
DEindices         0  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGlzdChmaXNoY29tYiRyYXdwdmFsLCBicmVha3M9MTAwLCBjb2w9XCJncmV5XCIsIG1haW49XCJGaXNoZXIgTWV0aG9kXCIsIHhsYWIgPSBcIlJhdyBwLXZhbHVlcyBFY290eXBlIGluIENvbnRyb2wgKG1ldGEtYW5hbHlzaXMpXCIpXG5gYGAifQ== -->

```r
hist(fishcomb$rawpval, breaks=100, col="grey", main="Fisher Method", xlab = "Raw p-values Ecotype in Control (meta-analysis)")
```

<!-- rnb-source-end -->

<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBQLXZhbHVlIGNvbWJpbmF0aW9uIHVzaW5nIGludmVyc2Ugbm9tcmFsIG1ldGhvZCBtZXRob2QgXG5pbnZub3JtY29tYiA8LSBpbnZub3JtKEVDX3Jhd3B2YWwsIG5yZXA9YygxMiwyMCksIEJIdGg9MC4wNSlcbnN1bW1hcnkoaW52bm9ybWNvbWIpXG5gYGAifQ== -->

```r
# P-value combination using inverse nomral method method 
invnormcomb <- invnorm(EC_rawpval, nrep=c(12,20), BHth=0.05)
summary(invnormcomb)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkRFaW5kaWNlcyAgICAgICAgIDAgIC1ub25lLSBudW1lcmljXG5UZXN0U3RhdGlzdGljIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucmF3cHZhbCAgICAgICAxMjg5NyAgLW5vbmUtIG51bWVyaWNcbmFkanB2YWwgICAgICAgMTI4OTcgIC1ub25lLSBudW1lcmljXG4ifQ== -->

```
              Length Class  Mode   
DEindices         0  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChpbnZub3JtY29tYilcbmBgYCJ9 -->

```r
head(invnormcomb)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin {"data":"$DEindices\ninteger(0)\n\n$TestStatistic\n   [1]  0.740096171  0.768752092  0.168763116  0.180931532 -0.203947965 -0.631725582\n   [7]  0.800810582 -0.907685980  0.089403786  0.554775172 -0.104740187  2.601449291\n  [13] -1.290284825 -1.055434667 -0.902380535 -0.667610936 -1.216617415  0.123134350\n  [19] -0.580516829  2.813463959 -1.137704846 -0.566375706 -0.348357711 -2.069468456\n  [25] -0.173052328 -0.846115568 -0.583941490  0.703765450  0.438612717  0.117177950\n  [31]  0.529485939  1.751910299  1.284609282  2.181563239  0.399839810 -0.588826443\n  [37] -0.174145074  1.279655677  0.916076107  0.119102371 -0.483115662  0.573042291\n  [43]  1.045851248  1.506252916  0.809223231 -0.312659425  0.611128697 -0.592316941\n  [49] -0.398111813  0.199873170 -1.591352735  0.268971437 -1.392316378 -0.852569496\n  [55]  0.893971648 -0.468444414  1.506973549  1.444195471 -0.093077882 -0.684753821\n  [61] -0.238464079 -1.262310430 -0.098005346 -0.801408173 -0.441941807  1.315263948\n  [67]  0.005862165  1.709296111 -1.698165536  0.758191980  1.081277446  1.559097324\n  [73] -0.299869107 -0.370292895  1.316154124  0.096373598 -1.083336912  1.232680535\n  [79] -0.325659973 -0.530980271  0.038344443 -0.139775798  2.761570850  0.157311465\n  [85] -1.023731478 -0.767964979 -1.102298643 -1.167683856 -0.128640667  0.346828968\n  [91]  0.271689285 -1.219034919 -1.529509238 -1.605717349  0.117147842 -0.241660258\n  [97] -1.231794023 -1.272961386  0.777303320  2.209051755  1.787342854 -1.377897016\n [103]  0.887505343 -0.382338609  0.025354525 -1.394314656 -0.130506922  0.375606077\n [109]  0.205338201 -0.602891288 -0.613378702  1.243498997 -1.323734018 -1.065233254\n [115] -0.158582816  0.774891582  0.655704442  0.408018928 -0.448397867 -0.973031695\n [121]  0.545514234 -0.881188330 -0.966057690 -2.399268061  0.000163886 -0.181336976\n [127]  1.180982861 -1.714659494 -0.551558077  0.086333460  0.325452547 -1.262969371\n [133]  0.715056981  0.016793498  0.762846118 -0.115354001 -0.113736452  2.670799457\n [139] -0.296119704  0.274827391 -0.224913214 -0.272874455 -1.019596272  0.067007519\n [145]  0.392758691  2.401716455 -1.298128713 -1.965410531  1.439884101  0.204080235\n [151]  0.909452565  1.381276688  1.537177852 -1.244879675  0.685309212 -1.567816674\n [157] -1.891920126 -0.623343954 -1.009643163  0.924813644  1.548018103  1.214309282\n [163] -0.192580469 -0.564079150  1.121481750 -0.531709647 -0.937147455  0.500299410\n [169]  0.347765867  1.177546541  0.165269584  1.062909250 -1.001755290 -0.514714448\n [175]  0.412881210 -1.597648038  0.114082883  1.150558911  1.605303219  1.396790103\n [181] -0.397463476  1.546010426  1.394290372  0.549045293 -0.635255611  0.435408608\n [187] -0.677646802  0.888899309 -1.490162817 -1.332302366 -2.600773973  1.064369942\n [193] -0.529426437  1.514497448 -0.302330524  0.443949951 -0.529760580  1.402405990\n [199]  0.600016593  0.838135942  0.360878082  0.535673157 -0.097078614 -0.451625296\n [205] -0.451322837 -1.544668516  1.041712875  0.002189505 -1.628331562  1.266515965\n [211]  0.384913884 -0.177927274 -0.155820529  0.009049078 -0.186209384  0.264447726\n [217] -0.011570489  0.796148300  1.144570557 -1.231590171 -1.001308860  1.401702737\n [223] -0.189203265  0.597337478 -1.361602165  0.967278722  0.337937725  0.112376217\n [229]  0.570203823  0.152244332  0.418053130 -0.232251135  1.234000683 -0.358206445\n [235] -0.143653193  0.692984975  0.440726037 -0.636989834  0.039019989  0.418562559\n [241] -0.408221507 -0.327024950  0.712026552  0.409409386 -0.674358828 -0.629721498\n [247]  0.275556579  1.457809881  0.486270935 -1.288522978 -1.502231868  0.072209416\n [253] -0.125977332 -0.286143735  1.429458213 -0.658380881  0.363654553 -0.331011501\n [259] -0.884367111 -0.101501747  0.176559815  0.113300466 -0.225998255 -1.455513166\n [265]  1.158653967 -1.467903997  1.408612299 -2.088556657  2.143243098  2.360238065\n [271]  0.827267326  0.085699620 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1.149250498  0.660911777\n [553]  1.973262843  0.956229415 -0.622928057  1.630740083 -0.220845625  1.190033459\n [559]  0.817828848  1.843435129  0.305889898  2.845714749 -1.150411868 -0.723359613\n [565] -0.355896996  0.012852148 -0.785221302  1.529575383 -1.890112335  0.482365302\n [571]  0.442492671 -0.630355400  0.024792202  2.071660422  0.792677288 -0.087896873\n [577]  0.181764747 -0.273427959  1.166550482 -2.201181021  1.844805728 -0.653352623\n [583]  2.509945490 -1.887908447 -0.743304593  0.024888094 -0.865176656  1.005927338\n [589] -0.370011777  0.381847901  0.369110266 -0.392433810  0.326035706  0.245213298\n [595] -0.923602742 -0.463610060 -2.080885111 -0.708992152 -0.560522036 -0.008532623\n [601]  1.178756404 -0.476944184  0.608870238 -1.248906301 -3.356615012 -1.394972637\n [607]  1.097350189  3.508751447  0.919293008 -2.301670031 -0.538159165 -0.297112693\n [613] -0.044359874  0.834985743  0.525132153 -0.609895182 -0.444763503  0.199953283\n [619]  0.857969790  0.451070343 -0.649654733  0.947415750 -0.446628922  0.071855390\n [625]  0.132205049  0.207136687  0.681566662 -0.406596664 -0.502687757  0.966769562\n [631] -0.754183536 -0.238533500  1.118144193  0.828918844  0.283841406  0.205106500\n [637] -0.498461148 -0.136923134 -0.071105455  0.602476252  0.697853158 -1.270167373\n [643] -1.604232003  0.984187265 -0.244921251  0.152447687  0.381426345 -0.808375975\n [649] -0.640973037  0.353095169  0.829347900  0.696707004  0.188852656  1.566551212\n [655]  2.137534607  1.069568733 -0.387557529 -0.115657069 -0.676796499 -1.279548703\n [661]  1.073630378 -2.070956198  1.264572369 -0.982718283 -0.049722337 -0.632960594\n [667] -1.413965172 -0.374963103 -0.360748935  1.132016301  0.612833976 -0.310913824\n [673] -0.727640508  1.169778693  0.761113359  0.305927223 -0.213896920 -0.047535395\n [679] -1.380094818  0.229120420  0.892235979 -1.789404943  0.269428512  1.396036004\n [685] -0.673834004 -0.275249957  0.034598326 -0.986339604 -0.651349124 -0.251984073\n [691] -0.436807993 -1.684635179 -1.159538087 -0.795949984  0.289623002 -0.911818764\n [697] -0.162704576  0.103041918  0.088425227  0.549574398  1.144125480  0.835462387\n [703]  0.579880997 -0.121773841 -0.681784727 -1.566744154  2.180987466 -0.038532573\n [709]  1.221030595  1.166731890  0.448358635  1.063025316  0.784264275 -0.432822273\n [715]  1.675155111 -0.197995099  0.261173262 -0.595691353  1.432326408 -0.608309174\n [721] -0.010286048  1.169578415  0.129517269  1.976466009 -0.084294840  0.142334594\n [727] -2.045505464  0.467225580 -0.556894377  0.724867746 -1.170730240  0.900584619\n [733] -0.118245485  1.008192770  0.427331729  1.117456666  0.437899913  1.673791549\n [739]  1.502166163  1.062265203  1.150675286  0.257156877  1.749910177  0.639772924\n [745]  0.927687704  1.181223248  0.171919659 -0.547598542 -1.209351719 -1.245682100\n [751] -1.064253926 -1.279755405 -0.356052814  1.099865286  0.511074108  0.015273852\n [757]  1.041987063  0.702189762 -1.089761651 -0.235422509  0.552935153  1.604643727\n [763] -0.923293732  1.465263593 -0.288331618 -0.077404162  0.373905080 -2.116281878\n [769] -0.350301849 -0.363517582  0.358730518  0.273178664 -0.803148296 -0.389994606\n [775] -0.550572600  0.272323881 -0.642118640  1.532856574 -0.485293011 -2.253957638\n [781] -1.352229529 -1.004082244 -0.525892202 -1.451697165 -0.785075201  1.267098592\n [787]  1.183904859 -0.704654420 -0.259742210 -0.217070066 -1.676819449 -2.171109591\n [793]  0.011344692 -0.945187268  0.557992349 -0.615686971 -0.711508383  0.989544810\n [799]  1.271703860 -1.200633204  0.701906408  1.273067261  0.251511308 -1.248835359\n [805]  0.766829574  0.506558586 -0.808213437 -0.117183975  0.587050593  0.627096594\n [811] -0.028952360 -2.095166454  1.474767950 -1.680796326 -0.505270069  0.055552995\n [817] -3.468439733 -1.926143666  0.762699071 -1.015408731 -1.119921586 -1.023144619\n [823] -0.274300284  0.302282177 -0.195955183 -1.250551395 -0.760873359  0.659410686\n [829] -0.623652448  0.187676425  0.817423290 -1.002863740  1.422262075  0.078238108\n [835]  1.519318112 -0.235862045 -0.255944463 -0.985405415  0.122638499  0.176288956\n [841] -0.040171517  0.292541293  0.247203204 -0.596531298 -0.619316168 -1.122621140\n [847] -0.604504033  0.147642208 -0.965788749 -0.469694477  0.997813811  1.604864593\n [853] -1.233977452  0.714815952 -0.961021537 -1.124002712  1.889145021  0.472807866\n [859]  0.058940768 -0.578996831 -0.119873453  0.094044580 -0.243725464 -0.286361754\n [865] -0.311125541 -0.350783943  0.978065071 -0.537549135 -0.064188883 -0.244411765\n [871] -0.459430819 -1.988484746  0.300131911 -0.451602441 -0.662047136  0.387585753\n [877]  1.328557323  1.524431821  0.062258484  0.560033810 -0.693781880  0.103241061\n [883] -0.623372829  1.048746381  0.419555219 -0.165335035 -0.701185432 -0.864713704\n [889]  0.363800219  0.289683129 -0.672660446 -0.053246088  0.208367828  1.107393750\n [895] -2.490272888 -0.492116229 -0.017088513 -0.205502629  0.191081454 -0.161089214\n [901] -1.190188944  1.291322274  0.790337046 -0.469725269  0.982994434 -0.697409757\n [907]  0.854444179  0.405628840 -1.160282442  0.156672769 -1.917595253 -0.490572008\n [913]  0.317647500 -0.030555358 -0.034713344 -2.335398357 -0.185245118 -0.270209059\n [919] -2.589324472  1.138272118  1.346733936  0.881054281 -0.560458135  0.815413310\n [925] -1.477317645 -0.375791167 -0.250590601 -1.044502221 -0.992909888  0.360570634\n [931] -0.466207701  0.587105996  0.740690882  0.791611627 -0.030685071 -0.161487266\n [937]  0.561840572 -0.397306281  0.358642175  0.580786074  0.480506817  1.146658647\n [943] -0.217569999 -0.212521125  0.282019837 -0.107860223  0.796496599 -0.020871082\n [949]  1.309472797 -0.483545242 -0.492243339  0.977998948 -0.099491340 -0.551541671\n [955] -1.597500284 -1.447269163  0.023982766 -1.123757253  0.210799607 -0.305529986\n [961]  0.094959245  0.343341058 -0.951738366  1.102421573  0.625298264  3.049297513\n [967] -0.624013435 -0.597391681 -0.028275005  0.375435096  0.459969971 -0.700483191\n [973]  0.174926802 -1.231034801 -0.349192443  0.779109730  0.708222538 -0.151662776\n [979]  0.854967834  0.666593437 -0.423822187 -0.047986678  1.275778225  1.455512005\n [985]  0.341934524 -1.464794661  0.887039357  0.982009656  2.179410106  0.089621685\n [991]  1.810553336  0.563546236  0.854741979 -1.072740080 -1.106621003 -1.008523210\n [997] -0.479057513 -0.650257764  1.136776203 -1.408758508\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n\n$rawpval\n   [1] 0.2296208209 0.2210202470 0.4329914856 0.4282106590 0.5808029190 0.7362168956\n   [7] 0.2116206558 0.8179779235 0.4643805074 0.2895242257 0.5417090135 0.0046415394\n  [13] 0.9015241081 0.8543867126 0.8165726213 0.7478090125 0.8881250932 0.4510003552\n  [19] 0.7192169293 0.0024505440 0.8723781255 0.7144307969 0.6362142211 0.9807489258\n  [25] 0.5686948520 0.8012558615 0.7203701612 0.2407894265 0.3304710905 0.4533595197\n  [31] 0.2982341980 0.0398946167 0.0994644267 0.0145708912 0.3446372536 0.7220111473\n  [37] 0.5691242747 0.1003331295 0.1798134894 0.4525971261 0.6854931929 0.2833080297\n  [43] 0.1478148578 0.0660011301 0.2091933774 0.6227302876 0.2705571916 0.7231808132\n  [49] 0.6547261165 0.4207898872 0.9442348925 0.3939758316 0.9180866930 0.8030509576\n  [55] 0.1856685305 0.6802665937 0.0659087186 0.0743419993 0.5370791557 0.7532503598\n  [61] 0.5942394137 0.8965814475 0.5390359759 0.7885523074 0.6707343418 0.0942106050\n  [67] 0.4976613481 0.0436980566 0.9552617384 0.2241680307 0.1397868585 0.0594866734\n  [73] 0.6178615003 0.6444178663 0.0940611590 0.4616119300 0.8606705465 0.1088474885\n  [79] 0.6276591842 0.7022837747 0.4847065282 0.5555814322 0.0028762017 0.4374996925\n  [85] 0.8470189385 0.7787460043 0.8648340705 0.8785328443 0.5511790071 0.3643599065\n  [91] 0.3929304703 0.8885845303 0.9369308747 0.9458319862 0.4533714491 0.5954782866\n  [97] 0.8909869818 0.8984841279 0.2184899185 0.0135855206 0.0369410458 0.9158824569\n [103] 0.1874034455 0.6488948897 0.4898860916 0.9183886965 0.5519173110 0.3536048861\n [109] 0.4186539501 0.7267094942 0.7301870175 0.1068420051 0.9072043019 0.8566148056\n [115] 0.5630012158 0.2192018633 0.2560071552 0.3416298893 0.6730669595 0.8347312252\n [121] 0.2926999468 0.8108920527 0.8329923428 0.9917860582 0.4999346190 0.5719484577\n [127] 0.1188047660 0.9567961630 0.7093744169 0.4656006702 0.3724192955 0.8966999063\n [133] 0.2372869173 0.4933006786 0.2227775867 0.5459177312 0.5452766422 0.0037835421\n [139] 0.6164306665 0.3917244265 0.5889766052 0.6075251382 0.8460400132 0.4732878587\n [145] 0.3472488586 0.0081591758 0.9028784454 0.9753166292 0.0749500959 0.4191453998\n [151] 0.1815556429 0.0835969516 0.0621248812 0.8934120084 0.2465744099 0.9415380388\n [157] 0.9707491881 0.7334707402 0.8436668590 0.1775314261 0.0608089690 0.1123148212\n [163] 0.5763562290 0.7136498664 0.1310414277 0.7025364452 0.8256586431 0.3084321346\n [169] 0.3640080128 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0.2884248045 0.7309494309 0.7616153711 0.1611983286\n [799] 0.1017391792 0.8850532425 0.2413687664 0.1014970876 0.4007094105 0.8941373509\n [805] 0.2215914253 0.3062322864 0.7905161380 0.5466428674 0.2785848643 0.2652979598\n [811] 0.5115487069 0.9819219009 0.0701374703 0.9535987588 0.6933154187 0.4778489555\n [817] 0.9997382550 0.9729567812 0.2228214422 0.8450444836 0.8686264105 0.8468802635\n [823] 0.6080730669 0.3812184821 0.5776773776 0.8944509035 0.7766336437 0.2548160411\n [829] 0.7335720707 0.4255651560 0.2068432852 0.8420366950 0.0774750921 0.4688193244\n [835] 0.0643412214 0.5932301394 0.6010031393 0.8377875146 0.4511966832 0.4300334690\n [841] 0.5160218071 0.3849363964 0.4023754830 0.7245898240 0.7321459524 0.8692007834\n [847] 0.7272457056 0.4413125716 0.8329250502 0.6807133428 0.1591848259 0.0542618036\n [853] 0.8913943467 0.2373613883 0.8317293305 0.8694940622 0.0294361996 0.3181751307\n [859] 0.4764996430 0.7187043441 0.5477083029 0.4625368720 0.5962782747 0.6126994678\n [865] 0.6221474073 0.6371247778 0.1640210709 0.7045558227 0.5255900852 0.5965440346\n [871] 0.6770375895 0.9766209478 0.3820382696 0.6742222943 0.7460294933 0.3491613068\n [877] 0.0919970299 0.0637004381 0.4751784945 0.2877281881 0.7560904960 0.4588858259\n [883] 0.7334802255 0.1471474306 0.3374052039 0.5656598579 0.7584063499 0.8064020269\n [889] 0.3580035994 0.3860293321 0.7494183314 0.5212320825 0.4174708871 0.1340618635\n [895] 0.9936177477 0.6886814099 0.5068169987 0.5814102777 0.4242308887 0.5639884323\n [901] 0.8830139312 0.0982959750 0.2146654785 0.6807243441 0.1628050890 0.7572268016\n [907] 0.1964294632 0.3425076674 0.8770330847 0.4377513744 0.9724188237 0.6881354057\n [913] 0.3753761694 0.5121879278 0.5138458398 0.9902386899 0.5734816088 0.6065002880\n [919] 0.9951917783 0.1275034364 0.0890329692 0.1891442206 0.7124165062 0.2074178842\n [925] 0.9302047448 0.6464639226 0.5989346753 0.8518734540 0.8396230648 0.3592102227\n [931] 0.6794665814 0.2785662606 0.2294404446 0.2142935827 0.5122396513 0.5641451801\n [937] 0.2871123233 0.6544291917 0.3599313958 0.2806923210 0.3154335284 0.1257613638\n [943] 0.5861179193 0.5841497506 0.3889641498 0.5429467149 0.2128717258 0.5083257527\n [949] 0.0951871233 0.6856456779 0.6887263348 0.1640374223 0.5396259181 0.7093687954\n [955] 0.9449228828 0.9260892239 0.4904331779 0.8694419897 0.4165218226 0.6200187324\n [961] 0.4621735991 0.3656709479 0.8293851566 0.1351392185 0.2658876595 0.0011468860\n [967] 0.7336906190 0.7248770467 0.5112785922 0.3536684538 0.3227688875 0.7581872005\n [973] 0.4305685751 0.8908450758 0.6365275803 0.2179575394 0.2394035361 0.5602735409\n [979] 0.1962844776 0.2525159312 0.6641522519 0.5191365703 0.1010169635 0.0727637846\n [985] 0.3662000836 0.9285115255 0.1875288558 0.1630475457 0.0146506080 0.4642939259\n [991] 0.0351050105 0.2865314928 0.1963470026 0.8583061239 0.8657710886 0.8433983244\n [997] 0.6840511428 0.7422371330 0.1278159269 0.9205467060\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n\n$adjpval\n   [1] 0.9276450 0.9276450 0.9548246 0.9548079 0.9739354 0.9776476 0.9276450 0.9852988 0.9578653\n  [10] 0.9405526 0.9701563 0.8036151 0.9910125 0.9865384 0.9852988 0.9776476 0.9893372 0.9578653\n  [19] 0.9776476 0.7524920 0.9889433 0.9776476 0.9776476 0.9991188 0.9739354 0.9832951 0.9776476\n  [28] 0.9276450 0.9471070 0.9578653 0.9425390 0.9194714 0.9211834 0.8323740 0.9471070 0.9776476\n  [37] 0.9739354 0.9211834 0.9256901 0.9578653 0.9776476 0.9400112 0.9231483 0.9211834 0.9276450\n  [46] 0.9759312 0.9363420 0.9776476 0.9776476 0.9548079 0.9939550 0.9504501 0.9920224 0.9832951\n  [55] 0.9270488 0.9776476 0.9211834 0.9211834 0.9695358 0.9776476 0.9739354 0.9901160 0.9700968\n  [64] 0.9815507 0.9776476 0.9211834 0.9595363 0.9211834 0.9958783 0.9276450 0.9211834 0.9211834\n  [73] 0.9759312 0.9776476 0.9211834 0.9578653 0.9870438 0.9211834 0.9765234 0.9776476 0.9578653\n  [82] 0.9721427 0.7892420 0.9557346 0.9862830 0.9798382 0.9875395 0.9893372 0.9716805 0.9471070\n  [91] 0.9501569 0.9893372 0.9928269 0.9942453 0.9578653 0.9739354 0.9894996 0.9904060 0.9276450\n [100] 0.8208171 0.9194714 0.9919692 0.9276450 0.9776476 0.9578653 0.9920224 0.9716805 0.9471070\n [109] 0.9548079 0.9776476 0.9776476 0.9211834 0.9919475 0.9865384 0.9739354 0.9276450 0.9321802\n [118] 0.9471070 0.9776476 0.9858777 0.9425390 0.9845562 0.9858777 0.9994451 0.9606312 0.9739354\n [127] 0.9211834 0.9959875 0.9776476 0.9578653 0.9471070 0.9901160 0.9276450 0.9584361 0.9276450\n [136] 0.9716419 0.9716419 0.8036151 0.9759312 0.9500315 0.9739354 0.9749151 0.9861968 0.9578653\n [145] 0.9471070 0.8036151 0.9911129 0.9983855 0.9211834 0.9548079 0.9256901 0.9211834 0.9211834\n [154] 0.9899885 0.9297893 0.9939550 0.9976186 0.9776476 0.9858777 0.9256901 0.9211834 0.9211834\n [163] 0.9739354 0.9776476 0.9211834 0.9776476 0.9858777 0.9466562 0.9471070 0.9211834 0.9548246\n [172] 0.9211834 0.9858777 0.9776476 0.9471070 0.9939550 0.9578653 0.9211834 0.9211834 0.9211834\n [181] 0.9776476 0.9211834 0.9211834 0.9417110 0.9776476 0.9471070 0.9776476 0.9276450 0.9920472\n [190] 0.9919475 0.9994451 0.9211834 0.9776476 0.9211834 0.9759312 0.9471070 0.9776476 0.9211834\n [199] 0.9371603 0.9276450 0.9471070 0.9425390 0.9700968 0.9776476 0.9776476 0.9932475 0.9231483\n [208] 0.9603451 0.9946473 0.9211834 0.9471070 0.9739354 0.9739354 0.9587765 0.9739354 0.9519806\n [217] 0.9618485 0.9276450 0.9211834 0.9894996 0.9858777 0.9211834 0.9739354 0.9371603 0.9919475\n [226] 0.9231483 0.9471070 0.9578653 0.9405526 0.9557346 0.9471070 0.9739354 0.9211834 0.9776476\n [235] 0.9728673 0.9276450 0.9471070 0.9776476 0.9578653 0.9471070 0.9776476 0.9765234 0.9276450\n [244] 0.9471070 0.9776476 0.9776476 0.9500315 0.9211834 0.9469569 0.9910125 0.9923433 0.9578653\n [253] 0.9716419 0.9757295 0.9211834 0.9776476 0.9471070 0.9765234 0.9845562 0.9700968 0.9548079\n [262] 0.9578653 0.9739354 0.9920472 0.9211834 0.9920472 0.9211834 0.9991188 0.8501345 0.8036151\n [271] 0.9276450 0.9578653 0.9983855 0.9776476 0.9231483 0.9994451 0.9759312 0.9276450 0.9276450\n [280] 0.9211834 0.9469249 0.9832951 0.9211834 0.9471070 0.9832951 0.9276450 0.9321802 0.9832951\n [289] 0.9739354 0.9776476 0.9858777 0.9716805 0.9759312 0.9587765 0.9920472 0.9749151 0.9211834\n [298] 0.9739354 0.9858777 0.9276450 0.9893372 0.9425390 0.9776476 0.9548079 0.9321802 0.9211834\n [307] 0.9231483 0.9739354 0.9982935 0.9299663 0.9776476 0.9211834 0.9471070 0.9740436 0.9776476\n [316] 0.9870438 0.9539146 0.9776476 0.9776476 0.9701563 0.9919475 0.9471070 0.9790309 0.9276450\n [325] 0.9211834 0.9211834 0.9371603 0.9471070 0.9243540 0.9776476 0.9939550 0.9458710 0.9471070\n [334] 0.9231483 0.9471070 0.9776476 0.9994451 0.9776476 0.9815690 0.9231483 0.9548079 0.9865384\n [343] 0.9959875 0.9489663 0.9776476 0.9211834 0.9276450 0.9510504 0.9231483 0.9855749 0.9578653\n [352] 0.9578653 0.9548079 0.9211834 0.9790309 0.9832951 0.9687699 0.9759312 0.9548079 0.9858777\n [361] 0.9276450 0.9861968 0.9901160 0.9231483 0.9211834 0.9776476 0.9776476 0.9776476 0.9211834\n [370] 0.9739354 0.9639698 0.9323014 0.9231483 0.9739354 0.8208171 0.9211834 0.8921239 0.9776476\n [379] 0.9276450 0.9776476 0.8921239 0.9405526 0.9231483 0.9919692 0.9776476 0.9578653 0.9533457\n [388] 0.9211834 0.9471070 0.9749151 0.9194714 0.9583320 0.8036151 0.9231483 0.9363420 0.9739354\n [397] 0.9776476 0.9776476 0.9858777 0.9276450 0.9618485 0.9749151 0.9896732 0.9584361 0.9276450\n [406] 0.9939550 0.9745118 0.9777967 0.9776476 0.9739354 0.9919475 0.9276450 0.9211834 0.9276450\n [415] 0.9739354 0.9776476 0.9211834 0.9211834 0.9231483 0.9231483 0.9587765 0.9256901 0.9765234\n [424] 0.9578653 0.9211834 0.8208171 0.9583320 0.9276450 0.9308724 0.9716805 0.9276450 0.9374772\n [433] 0.8937374 0.9276450 0.9776476 0.9615470 0.9832951 0.9211834 0.9231483 0.9911129 0.9276450\n [442] 0.9988896 0.9776476 0.9587765 0.9776476 0.9695358 0.9211834 0.9578653 0.9578653 0.9776476\n [451] 0.9471070 0.9776476 0.9776476 0.9578653 0.9211834 0.9618485 0.9471070 0.9852988 0.9211834\n [460] 0.9489663 0.9425390 0.9548079 0.9471070 0.9471070 0.9500315 0.9276450 0.9858777 0.9471070\n [469] 0.9920472 0.9986050 0.9194714 0.9548246 0.9276450 0.9471070 0.9716419 0.9539146 0.9776476\n [478] 0.9919475 0.8036151 0.9776476 0.9256901 0.9832951 0.8036151 0.9988726 0.9826924 0.8323740\n [487] 0.9901160 0.9276450 0.9739354 0.9211834 0.9471070 0.9231483 0.9578653 0.9871023 0.9578653\n [496] 0.9692737 0.9276450 0.9471070 0.9869149 0.9919475 0.9276450 0.9375339 0.9471070 0.9548079\n [505] 0.9471070 0.9211834 0.9471070 0.9975963 0.9794740 0.9471070 0.9211834 0.9308724 0.9231483\n [514] 0.9939550 0.9211834 0.9776476 0.9231483 0.9794740 0.9776476 0.9471070 0.9578653 0.9776476\n [523] 0.9989539 0.9548079 0.9471070 0.9975985 0.9826924 0.9759312 0.9858777 0.9276450 0.9776476\n [532] 0.9776476 0.9548079 0.9578653 0.9548079 0.9893372 0.9211834 0.9860282 0.9776476 0.9264070\n [541] 0.9776476 0.9570663 0.9994451 0.9707022 0.9994451 0.9231483 0.9739354 0.9371603 0.9143439\n [550] 0.9211834 0.9211834 0.9321802 0.8921239 0.9243540 0.9776476 0.9211834 0.9739354 0.9211834\n [559] 0.9276450 0.9194714 0.9471070 0.7239064 0.9892987 0.9776476 0.9776476 0.9587765 0.9810947\n [568] 0.9211834 0.9976186 0.9471070 0.9471070 0.9776476 0.9578653 0.8889947 0.9276450 0.9692737\n [577] 0.9548079 0.9749151 0.9211834 0.9991188 0.9194714 0.9776476 0.8036151 0.9976186 0.9787961\n [586] 0.9578653 0.9832951 0.9231483 0.9776476 0.9471070 0.9471070 0.9776476 0.9471070 0.9541878\n [595] 0.9858777 0.9776476 0.9991188 0.9776476 0.9776476 0.9618485 0.9211834 0.9776476 0.9363420\n [604] 0.9899885 0.9998397 0.9920224 0.9211834 0.4165295 0.9256901 0.9994451 0.9776476 0.9759312\n [613] 0.9639874 0.9276450 0.9429875 0.9776476 0.9776476 0.9548079 0.9276450 0.9471070 0.9776476\n [622] 0.9256901 0.9776476 0.9578653 0.9578653 0.9548079 0.9303398 0.9776476 0.9776476 0.9231483\n [631] 0.9790309 0.9739354 0.9211834 0.9276450 0.9492827 0.9548079 0.9776476 0.9716805 0.9668655\n [640] 0.9371603 0.9276450 0.9901970 0.9942091 0.9231483 0.9739354 0.9557346 0.9471070 0.9818846\n [649] 0.9776476 0.9471070 0.9276450 0.9276450 0.9548079 0.9211834 0.8548071 0.9211834 0.9776476\n [658] 0.9716419 0.9776476 0.9907952 0.9211834 0.9991188 0.9211834 0.9858777 0.9643590 0.9776476\n [667] 0.9920472 0.9776476 0.9776476 0.9211834 0.9363420 0.9759312 0.9777967 0.9211834 0.9276450\n [676] 0.9471070 0.9739354 0.9639874 0.9919692 0.9548079 0.9276450 0.9966488 0.9504501 0.9211834\n [685] 0.9776476 0.9749151 0.9578653 0.9858777 0.9776476 0.9739354 0.9776476 0.9955784 0.9893372\n [694] 0.9815507 0.9483651 0.9852988 0.9739354 0.9578653 0.9578653 0.9413603 0.9211834 0.9276450\n [703] 0.9375339 0.9716419 0.9776476 0.9939550 0.8323740 0.9639698 0.9211834 0.9211834 0.9471070\n [712] 0.9211834 0.9276450 0.9776476 0.9211834 0.9739354 0.9529476 0.9776476 0.9211834 0.9776476\n [721] 0.9618485 0.9211834 0.9578653 0.8921239 0.9687699 0.9575450 0.9991188 0.9471070 0.9776476\n [730] 0.9276450 0.9893372 0.9264070 0.9716419 0.9231483 0.9471070 0.9211834 0.9471070 0.9211834\n [739] 0.9211834 0.9211834 0.9211834 0.9539146 0.9194714 0.9339651 0.9256901 0.9211834 0.9548079\n [748] 0.9776476 0.9893372 0.9899885 0.9865384 0.9907952 0.9776476 0.9211834 0.9447140 0.9587472\n [757] 0.9231483 0.9276450 0.9871023 0.9739354 0.9413207 0.9211834 0.9858777 0.9211834 0.9757295\n [766] 0.9676833 0.9471070 0.9991188 0.9776476 0.9776476 0.9471070 0.9500315 0.9815507 0.9776476\n [775] 0.9776476 0.9501569 0.9776476 0.9211834 0.9776476 0.9994451 0.9919475 0.9858777 0.9776476\n [784] 0.9920472 0.9810947 0.9211834 0.9211834 0.9776476 0.9739354 0.9739354 0.9955109 0.9991188\n [793] 0.9587765 0.9858777 0.9405526 0.9776476 0.9776476 0.9231483 0.9211834 0.9893372 0.9276450\n [802] 0.9211834 0.9539146 0.9899885 0.9276450 0.9455797 0.9818846 0.9716419 0.9371603 0.9339651\n [811] 0.9638328 0.9991188 0.9211834 0.9955273 0.9776476 0.9578653 0.9998397 0.9982935 0.9276450\n [820] 0.9860254 0.9885242 0.9862830 0.9749151 0.9471070 0.9739354 0.9899885 0.9794729 0.9321802\n [829] 0.9776476 0.9548079 0.9276450 0.9858777 0.9211834 0.9578653 0.9211834 0.9739354 0.9739354\n [838] 0.9858777 0.9578653 0.9548079 0.9639698 0.9479618 0.9541878 0.9776476 0.9776476 0.9885242\n [847] 0.9776476 0.9573616 0.9858777 0.9776476 0.9231483 0.9211834 0.9896732 0.9276450 0.9858777\n [856] 0.9885283 0.9143439 0.9471070 0.9578653 0.9776476 0.9716419 0.9578653 0.9739354 0.9757295\n [865] 0.9759312 0.9776476 0.9231483 0.9776476 0.9665227 0.9739354 0.9776476 0.9986110 0.9471070\n [874] 0.9776476 0.9776476 0.9471070 0.9211834 0.9211834 0.9578653 0.9405526 0.9776476 0.9578653\n [883] 0.9776476 0.9231483 0.9471070 0.9739354 0.9776476 0.9832951 0.9471070 0.9483651 0.9776476\n [892] 0.9643684 0.9548079 0.9211834 0.9994451 0.9776476 0.9618485 0.9739354 0.9548079 0.9739354\n [901] 0.9893372 0.9211834 0.9276450 0.9776476 0.9231483 0.9776476 0.9276450 0.9471070 0.9893372\n [910] 0.9557346 0.9982129 0.9776476 0.9471070 0.9638328 0.9638446 0.9994451 0.9739354 0.9749151\n [919] 0.9994451 0.9211834 0.9211834 0.9276450 0.9776476 0.9276450 0.9920472 0.9776476 0.9739354\n [928] 0.9865384 0.9858777 0.9471070 0.9776476 0.9371603 0.9276450 0.9276450 0.9638328 0.9739354\n [937] 0.9405526 0.9776476 0.9471070 0.9375339 0.9471070 0.9211834 0.9739354 0.9739354 0.9495213\n [946] 0.9705314 0.9276450 0.9618485 0.9211834 0.9776476 0.9776476 0.9231483 0.9700968 0.9776476\n [955] 0.9939550 0.9920472 0.9580700 0.9885283 0.9548079 0.9759312 0.9578653 0.9471070 0.9858777\n [964] 0.9211834 0.9339651 0.6431039 0.9776476 0.9776476 0.9638328 0.9471070 0.9471070 0.9776476\n [973] 0.9548079 0.9894996 0.9776476 0.9276450 0.9276450 0.9739354 0.9276450 0.9321802 0.9776476\n [982] 0.9639874 0.9211834 0.9211834 0.9471070 0.9920472 0.9276450 0.9231483 0.8323740 0.9578653\n [991] 0.9194714 0.9405526 0.9276450 0.9869149 0.9877574 0.9858777 0.9776476 0.9776476 0.9211834\n[1000] 0.9920472\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n"} -->

```
$DEindices
integer(0)

$TestStatistic
   [1]  0.740096171  0.768752092  0.168763116  0.180931532 -0.203947965 -0.631725582
   [7]  0.800810582 -0.907685980  0.089403786  0.554775172 -0.104740187  2.601449291
  [13] -1.290284825 -1.055434667 -0.902380535 -0.667610936 -1.216617415  0.123134350
  [19] -0.580516829  2.813463959 -1.137704846 -0.566375706 -0.348357711 -2.069468456
  [25] -0.173052328 -0.846115568 -0.583941490  0.703765450  0.438612717  0.117177950
  [31]  0.529485939  1.751910299  1.284609282  2.181563239  0.399839810 -0.588826443
  [37] -0.174145074  1.279655677  0.916076107  0.119102371 -0.483115662  0.573042291
  [43]  1.045851248  1.506252916  0.809223231 -0.312659425  0.611128697 -0.592316941
  [49] -0.398111813  0.199873170 -1.591352735  0.268971437 -1.392316378 -0.852569496
  [55]  0.893971648 -0.468444414  1.506973549  1.444195471 -0.093077882 -0.684753821
  [61] -0.238464079 -1.262310430 -0.098005346 -0.801408173 -0.441941807  1.315263948
  [67]  0.005862165  1.709296111 -1.698165536  0.758191980  1.081277446  1.559097324
  [73] -0.299869107 -0.370292895  1.316154124  0.096373598 -1.083336912  1.232680535
  [79] -0.325659973 -0.530980271  0.038344443 -0.139775798  2.761570850  0.157311465
  [85] -1.023731478 -0.767964979 -1.102298643 -1.167683856 -0.128640667  0.346828968
  [91]  0.271689285 -1.219034919 -1.529509238 -1.605717349  0.117147842 -0.241660258
  [97] -1.231794023 -1.272961386  0.777303320  2.209051755  1.787342854 -1.377897016
 [103]  0.887505343 -0.382338609  0.025354525 -1.394314656 -0.130506922  0.375606077
 [109]  0.205338201 -0.602891288 -0.613378702  1.243498997 -1.323734018 -1.065233254
 [115] -0.158582816  0.774891582  0.655704442  0.408018928 -0.448397867 -0.973031695
 [121]  0.545514234 -0.881188330 -0.966057690 -2.399268061  0.000163886 -0.181336976
 [127]  1.180982861 -1.714659494 -0.551558077  0.086333460  0.325452547 -1.262969371
 [133]  0.715056981  0.016793498  0.762846118 -0.115354001 -0.113736452  2.670799457
 [139] -0.296119704  0.274827391 -0.224913214 -0.272874455 -1.019596272  0.067007519
 [145]  0.392758691  2.401716455 -1.298128713 -1.965410531  1.439884101  0.204080235
 [151]  0.909452565  1.381276688  1.537177852 -1.244879675  0.685309212 -1.567816674
 [157] -1.891920126 -0.623343954 -1.009643163  0.924813644  1.548018103  1.214309282
 [163] -0.192580469 -0.564079150  1.121481750 -0.531709647 -0.937147455  0.500299410
 [169]  0.347765867  1.177546541  0.165269584  1.062909250 -1.001755290 -0.514714448
 [175]  0.412881210 -1.597648038  0.114082883  1.150558911  1.605303219  1.396790103
 [181] -0.397463476  1.546010426  1.394290372  0.549045293 -0.635255611  0.435408608
 [187] -0.677646802  0.888899309 -1.490162817 -1.332302366 -2.600773973  1.064369942
 [193] -0.529426437  1.514497448 -0.302330524  0.443949951 -0.529760580  1.402405990
 [199]  0.600016593  0.838135942  0.360878082  0.535673157 -0.097078614 -0.451625296
 [205] -0.451322837 -1.544668516  1.041712875  0.002189505 -1.628331562  1.266515965
 [211]  0.384913884 -0.177927274 -0.155820529  0.009049078 -0.186209384  0.264447726
 [217] -0.011570489  0.796148300  1.144570557 -1.231590171 -1.001308860  1.401702737
 [223] -0.189203265  0.597337478 -1.361602165  0.967278722  0.337937725  0.112376217
 [229]  0.570203823  0.152244332  0.418053130 -0.232251135  1.234000683 -0.358206445
 [235] -0.143653193  0.692984975  0.440726037 -0.636989834  0.039019989  0.418562559
 [241] -0.408221507 -0.327024950  0.712026552  0.409409386 -0.674358828 -0.629721498
 [247]  0.275556579  1.457809881  0.486270935 -1.288522978 -1.502231868  0.072209416
 [253] -0.125977332 -0.286143735  1.429458213 -0.658380881  0.363654553 -0.331011501
 [259] -0.884367111 -0.101501747  0.176559815  0.113300466 -0.225998255 -1.455513166
 [265]  1.158653967 -1.467903997  1.408612299 -2.088556657  2.143243098  2.360238065
 [271]  0.827267326  0.085699620 -1.964682192 -0.611471832  1.038837356 -2.318150018
 [277] -0.313448198  0.859276925  0.803978093  1.588981640  0.488958964 -0.866700559
 [283]  1.125425857  0.325796979 -0.864029566  0.798192866  0.664863237 -0.859984905
 [289] -0.245351830 -0.522847037 -0.950540526 -0.134426015 -0.315337717  0.009777150
 [295] -1.451513398 -0.277742885  1.150951531 -0.208955455 -1.006842730  0.831236885
 [301] -1.226516464  0.542016856 -0.455369584  0.173161836  0.665127201  1.103982936
 [307]  1.000348448 -0.212290195 -1.929213756  0.682949827 -0.600804204  1.123109107
 [313]  0.421762891 -0.260310981 -0.633669905 -1.084097177  0.255175646 -0.581310232
 [319] -0.367446443 -0.106730492 -1.323219383  0.444617396 -0.755107145  0.783995286
 [325]  1.672525484  1.486285530  0.593213393  0.299598116  0.956489715 -0.377043522
 [331] -1.586055124  0.503668495  0.474365458  1.050688773  0.425517153 -0.345150363
 [337] -2.588225918 -0.407779082 -0.806305920  0.969751597  0.191797819 -1.057291425
 [343] -1.733982816  0.286800888 -0.557232253  1.348299635  0.817606610  0.267932853
 [349]  1.028663995 -0.914979118  0.034583834  0.056736274  0.208077882  1.433355197
 [355] -0.753733457 -0.863578222 -0.083372913 -0.319384333  0.230814152 -0.975258812
 [361]  0.762439282 -1.019371964 -1.257981290  1.047940904  1.074729743 -0.643653259
 [367] -0.612833094 -0.623477276  1.409814132 -0.203732679 -0.039228215  0.652540214
 [373]  1.034378673 -0.183308029  2.231687620  1.382395418  2.034279983 -0.522370570
 [379]  0.744560100 -0.473986996  1.979429420  0.556964697  1.052157957 -1.373840875
 [385] -0.468838190  0.102377831  0.259636831  1.403281150  0.421384310 -0.279085515
 [391]  1.803293732  0.020839942  2.590391541  1.017273270  0.609139601 -0.232199175
 [397] -0.574348925 -0.521733839 -0.943107305  0.706863241 -0.018249663 -0.268393615
 [403] -1.234088859  0.019088574  0.764240353 -1.579901917 -0.263806818 -0.730866082
 [409] -0.659231277 -0.251310257 -1.321801389  0.725357610  1.299211338  0.866910133
 [415] -0.248942343 -0.493123633  1.735526721  1.371817618  0.974121706  0.970677059
 [421]  0.013135146  0.906525083 -0.327799866  0.113676613  1.308455022  2.195659263
 [427]  0.023094535  0.804536432  0.676556122 -0.134269819  0.782609099  0.584785564
 [433]  1.941062018  0.731243773 -0.560791486 -0.005018886 -0.854164864  1.326060546
 [439]  0.988286749 -1.297608382  0.744796078 -2.027118256 -0.625137629  0.010939860
 [445] -0.595395283 -0.089357403  1.208154348  0.026207016  0.051152546 -0.385732694
 [451]  0.431798297 -0.473454571 -0.663136977  0.026050254  1.262292419 -0.014713762
 [457]  0.461627090 -0.913522791  1.379563451  0.286189197  0.541252388  0.186530333
 [463]  0.425989114  0.451284064  0.275097627  0.838281777 -0.930672881  0.449398571
 [469] -1.462567667 -1.983366634  1.775372370  0.166229310  0.714440246  0.391642938
 [475] -0.118799076  0.252410397 -0.714348244 -1.350997928  2.644960729 -0.622214768
 [481]  0.938426146 -0.859514949  2.428929456 -1.997463220 -0.825930793  2.185888837
 [487] -1.261107784  0.725039186 -0.153229279  1.381120777  0.394265396  1.041925365
 [493]  0.038859597 -1.090754953  0.127295531 -0.088185960  0.773563149  0.366056146
 [499] -1.072147302 -1.336490653  0.792932564  0.581435669  0.345216981  0.230439767
 [505]  0.443866098  1.229246328  0.349829315 -1.844287323 -0.765269441  0.299196777
 [511]  1.150962972  0.676060760  1.034521869 -1.586825677  1.201205158 -0.476602659
 [517]  1.020645358 -0.762116800 -0.385846193  0.416325409  0.133790238 -0.710828586
 [523] -2.031399118  0.188049487  0.327586590 -1.875898307 -0.828006558 -0.315815950
 [529] -1.013255173  0.822831128 -0.502704533 -0.417170290  0.215089338  0.073300656
 [535]  0.221129338 -1.180240334  1.202190972 -1.015739843 -0.589016749  0.898719561
 [541] -0.594083873  0.149059564 -2.371260768 -0.109575428 -2.251985880  1.016696401
 [547] -0.172455405  0.591570277  1.874554279  1.310926411  1.149250498  0.660911777
 [553]  1.973262843  0.956229415 -0.622928057  1.630740083 -0.220845625  1.190033459
 [559]  0.817828848  1.843435129  0.305889898  2.845714749 -1.150411868 -0.723359613
 [565] -0.355896996  0.012852148 -0.785221302  1.529575383 -1.890112335  0.482365302
 [571]  0.442492671 -0.630355400  0.024792202  2.071660422  0.792677288 -0.087896873
 [577]  0.181764747 -0.273427959  1.166550482 -2.201181021  1.844805728 -0.653352623
 [583]  2.509945490 -1.887908447 -0.743304593  0.024888094 -0.865176656  1.005927338
 [589] -0.370011777  0.381847901  0.369110266 -0.392433810  0.326035706  0.245213298
 [595] -0.923602742 -0.463610060 -2.080885111 -0.708992152 -0.560522036 -0.008532623
 [601]  1.178756404 -0.476944184  0.608870238 -1.248906301 -3.356615012 -1.394972637
 [607]  1.097350189  3.508751447  0.919293008 -2.301670031 -0.538159165 -0.297112693
 [613] -0.044359874  0.834985743  0.525132153 -0.609895182 -0.444763503  0.199953283
 [619]  0.857969790  0.451070343 -0.649654733  0.947415750 -0.446628922  0.071855390
 [625]  0.132205049  0.207136687  0.681566662 -0.406596664 -0.502687757  0.966769562
 [631] -0.754183536 -0.238533500  1.118144193  0.828918844  0.283841406  0.205106500
 [637] -0.498461148 -0.136923134 -0.071105455  0.602476252  0.697853158 -1.270167373
 [643] -1.604232003  0.984187265 -0.244921251  0.152447687  0.381426345 -0.808375975
 [649] -0.640973037  0.353095169  0.829347900  0.696707004  0.188852656  1.566551212
 [655]  2.137534607  1.069568733 -0.387557529 -0.115657069 -0.676796499 -1.279548703
 [661]  1.073630378 -2.070956198  1.264572369 -0.982718283 -0.049722337 -0.632960594
 [667] -1.413965172 -0.374963103 -0.360748935  1.132016301  0.612833976 -0.310913824
 [673] -0.727640508  1.169778693  0.761113359  0.305927223 -0.213896920 -0.047535395
 [679] -1.380094818  0.229120420  0.892235979 -1.789404943  0.269428512  1.396036004
 [685] -0.673834004 -0.275249957  0.034598326 -0.986339604 -0.651349124 -0.251984073
 [691] -0.436807993 -1.684635179 -1.159538087 -0.795949984  0.289623002 -0.911818764
 [697] -0.162704576  0.103041918  0.088425227  0.549574398  1.144125480  0.835462387
 [703]  0.579880997 -0.121773841 -0.681784727 -1.566744154  2.180987466 -0.038532573
 [709]  1.221030595  1.166731890  0.448358635  1.063025316  0.784264275 -0.432822273
 [715]  1.675155111 -0.197995099  0.261173262 -0.595691353  1.432326408 -0.608309174
 [721] -0.010286048  1.169578415  0.129517269  1.976466009 -0.084294840  0.142334594
 [727] -2.045505464  0.467225580 -0.556894377  0.724867746 -1.170730240  0.900584619
 [733] -0.118245485  1.008192770  0.427331729  1.117456666  0.437899913  1.673791549
 [739]  1.502166163  1.062265203  1.150675286  0.257156877  1.749910177  0.639772924
 [745]  0.927687704  1.181223248  0.171919659 -0.547598542 -1.209351719 -1.245682100
 [751] -1.064253926 -1.279755405 -0.356052814  1.099865286  0.511074108  0.015273852
 [757]  1.041987063  0.702189762 -1.089761651 -0.235422509  0.552935153  1.604643727
 [763] -0.923293732  1.465263593 -0.288331618 -0.077404162  0.373905080 -2.116281878
 [769] -0.350301849 -0.363517582  0.358730518  0.273178664 -0.803148296 -0.389994606
 [775] -0.550572600  0.272323881 -0.642118640  1.532856574 -0.485293011 -2.253957638
 [781] -1.352229529 -1.004082244 -0.525892202 -1.451697165 -0.785075201  1.267098592
 [787]  1.183904859 -0.704654420 -0.259742210 -0.217070066 -1.676819449 -2.171109591
 [793]  0.011344692 -0.945187268  0.557992349 -0.615686971 -0.711508383  0.989544810
 [799]  1.271703860 -1.200633204  0.701906408  1.273067261  0.251511308 -1.248835359
 [805]  0.766829574  0.506558586 -0.808213437 -0.117183975  0.587050593  0.627096594
 [811] -0.028952360 -2.095166454  1.474767950 -1.680796326 -0.505270069  0.055552995
 [817] -3.468439733 -1.926143666  0.762699071 -1.015408731 -1.119921586 -1.023144619
 [823] -0.274300284  0.302282177 -0.195955183 -1.250551395 -0.760873359  0.659410686
 [829] -0.623652448  0.187676425  0.817423290 -1.002863740  1.422262075  0.078238108
 [835]  1.519318112 -0.235862045 -0.255944463 -0.985405415  0.122638499  0.176288956
 [841] -0.040171517  0.292541293  0.247203204 -0.596531298 -0.619316168 -1.122621140
 [847] -0.604504033  0.147642208 -0.965788749 -0.469694477  0.997813811  1.604864593
 [853] -1.233977452  0.714815952 -0.961021537 -1.124002712  1.889145021  0.472807866
 [859]  0.058940768 -0.578996831 -0.119873453  0.094044580 -0.243725464 -0.286361754
 [865] -0.311125541 -0.350783943  0.978065071 -0.537549135 -0.064188883 -0.244411765
 [871] -0.459430819 -1.988484746  0.300131911 -0.451602441 -0.662047136  0.387585753
 [877]  1.328557323  1.524431821  0.062258484  0.560033810 -0.693781880  0.103241061
 [883] -0.623372829  1.048746381  0.419555219 -0.165335035 -0.701185432 -0.864713704
 [889]  0.363800219  0.289683129 -0.672660446 -0.053246088  0.208367828  1.107393750
 [895] -2.490272888 -0.492116229 -0.017088513 -0.205502629  0.191081454 -0.161089214
 [901] -1.190188944  1.291322274  0.790337046 -0.469725269  0.982994434 -0.697409757
 [907]  0.854444179  0.405628840 -1.160282442  0.156672769 -1.917595253 -0.490572008
 [913]  0.317647500 -0.030555358 -0.034713344 -2.335398357 -0.185245118 -0.270209059
 [919] -2.589324472  1.138272118  1.346733936  0.881054281 -0.560458135  0.815413310
 [925] -1.477317645 -0.375791167 -0.250590601 -1.044502221 -0.992909888  0.360570634
 [931] -0.466207701  0.587105996  0.740690882  0.791611627 -0.030685071 -0.161487266
 [937]  0.561840572 -0.397306281  0.358642175  0.580786074  0.480506817  1.146658647
 [943] -0.217569999 -0.212521125  0.282019837 -0.107860223  0.796496599 -0.020871082
 [949]  1.309472797 -0.483545242 -0.492243339  0.977998948 -0.099491340 -0.551541671
 [955] -1.597500284 -1.447269163  0.023982766 -1.123757253  0.210799607 -0.305529986
 [961]  0.094959245  0.343341058 -0.951738366  1.102421573  0.625298264  3.049297513
 [967] -0.624013435 -0.597391681 -0.028275005  0.375435096  0.459969971 -0.700483191
 [973]  0.174926802 -1.231034801 -0.349192443  0.779109730  0.708222538 -0.151662776
 [979]  0.854967834  0.666593437 -0.423822187 -0.047986678  1.275778225  1.455512005
 [985]  0.341934524 -1.464794661  0.887039357  0.982009656  2.179410106  0.089621685
 [991]  1.810553336  0.563546236  0.854741979 -1.072740080 -1.106621003 -1.008523210
 [997] -0.479057513 -0.650257764  1.136776203 -1.408758508
 [ reached getOption("max.print") -- omitted 11897 entries ]

$rawpval
   [1] 0.2296208209 0.2210202470 0.4329914856 0.4282106590 0.5808029190 0.7362168956
   [7] 0.2116206558 0.8179779235 0.4643805074 0.2895242257 0.5417090135 0.0046415394
  [13] 0.9015241081 0.8543867126 0.8165726213 0.7478090125 0.8881250932 0.4510003552
  [19] 0.7192169293 0.0024505440 0.8723781255 0.7144307969 0.6362142211 0.9807489258
  [25] 0.5686948520 0.8012558615 0.7203701612 0.2407894265 0.3304710905 0.4533595197
  [31] 0.2982341980 0.0398946167 0.0994644267 0.0145708912 0.3446372536 0.7220111473
  [37] 0.5691242747 0.1003331295 0.1798134894 0.4525971261 0.6854931929 0.2833080297
  [43] 0.1478148578 0.0660011301 0.2091933774 0.6227302876 0.2705571916 0.7231808132
  [49] 0.6547261165 0.4207898872 0.9442348925 0.3939758316 0.9180866930 0.8030509576
  [55] 0.1856685305 0.6802665937 0.0659087186 0.0743419993 0.5370791557 0.7532503598
  [61] 0.5942394137 0.8965814475 0.5390359759 0.7885523074 0.6707343418 0.0942106050
  [67] 0.4976613481 0.0436980566 0.9552617384 0.2241680307 0.1397868585 0.0594866734
  [73] 0.6178615003 0.6444178663 0.0940611590 0.4616119300 0.8606705465 0.1088474885
  [79] 0.6276591842 0.7022837747 0.4847065282 0.5555814322 0.0028762017 0.4374996925
  [85] 0.8470189385 0.7787460043 0.8648340705 0.8785328443 0.5511790071 0.3643599065
  [91] 0.3929304703 0.8885845303 0.9369308747 0.9458319862 0.4533714491 0.5954782866
  [97] 0.8909869818 0.8984841279 0.2184899185 0.0135855206 0.0369410458 0.9158824569
 [103] 0.1874034455 0.6488948897 0.4898860916 0.9183886965 0.5519173110 0.3536048861
 [109] 0.4186539501 0.7267094942 0.7301870175 0.1068420051 0.9072043019 0.8566148056
 [115] 0.5630012158 0.2192018633 0.2560071552 0.3416298893 0.6730669595 0.8347312252
 [121] 0.2926999468 0.8108920527 0.8329923428 0.9917860582 0.4999346190 0.5719484577
 [127] 0.1188047660 0.9567961630 0.7093744169 0.4656006702 0.3724192955 0.8966999063
 [133] 0.2372869173 0.4933006786 0.2227775867 0.5459177312 0.5452766422 0.0037835421
 [139] 0.6164306665 0.3917244265 0.5889766052 0.6075251382 0.8460400132 0.4732878587
 [145] 0.3472488586 0.0081591758 0.9028784454 0.9753166292 0.0749500959 0.4191453998
 [151] 0.1815556429 0.0835969516 0.0621248812 0.8934120084 0.2465744099 0.9415380388
 [157] 0.9707491881 0.7334707402 0.8436668590 0.1775314261 0.0608089690 0.1123148212
 [163] 0.5763562290 0.7136498664 0.1310414277 0.7025364452 0.8256586431 0.3084321346
 [169] 0.3640080128 0.1194887164 0.4343658990 0.1439115540 0.8417691021 0.6966237120
 [175] 0.3398468241 0.9449393354 0.4545860456 0.1249568725 0.0542135455 0.0812383497
 [181] 0.6544871423 0.0610510249 0.0816149686 0.2914871837 0.7373691404 0.3316329317
 [187] 0.7510021680 0.1870285984 0.9319092847 0.9086195749 0.9953493143 0.1435805725
 [193] 0.7017451686 0.0649498603 0.6187999444 0.3285393845 0.7018610301 0.0803970228
 [199] 0.2742475887 0.2009771793 0.3590952945 0.2960922292 0.5386680181 0.6742305282
 [205] 0.6741215561 0.9387867680 0.1487724089 0.4991265147 0.9482726997 0.1026642087
 [211] 0.3501506095 0.5706099546 0.5619127558 0.4963899895 0.5738597172 0.3957174701
 [217] 0.5046158543 0.2129729215 0.1261935473 0.8909488928 0.8416612447 0.0805020166
 [223] 0.5750332455 0.2751410436 0.9133382642 0.1667023588 0.3677050591 0.4552625561
 [229] 0.2842697319 0.4394971149 0.3379541362 0.5918285182 0.1086013249 0.6399055879
 [235] 0.5571128327 0.2441594902 0.3297056725 0.7379342674 0.4844372259 0.3377679287
 [241] 0.6584444702 0.6281754929 0.2382241671 0.3411196262 0.7499583940 0.7355615928
 [247] 0.3914443318 0.0724464770 0.3133875418 0.9012180122 0.9334813814 0.4712176261
 [253] 0.5501250658 0.6126159823 0.0764362886 0.7448532912 0.3580579921 0.6296821010
 [259] 0.8117509645 0.5404239148 0.4299270807 0.4548961740 0.5893986114 0.9272363760
 [265] 0.1232986309 0.9289348489 0.0794749201 0.9816261749 0.0160467917 0.0091316053
 [271] 0.2040427794 0.4658526021 0.9752744838 0.7295563697 0.1494401923 0.9897794153
 [277] 0.6230299143 0.1950938759 0.2107048136 0.0560322689 0.3124353749 0.8069469503
 [283] 0.1302043097 0.3722889821 0.8062141755 0.2123792884 0.2530689857 0.8051013183
 [289] 0.5969079882 0.6994596481 0.8290811641 0.5534671443 0.6237473745 0.4960995436
 [295] 0.9266815228 0.6093951311 0.1248760881 0.5827584937 0.8429948216 0.2029199109
 [301] 0.8899978083 0.2939034469 0.6755783132 0.4312621104 0.2529845690 0.1348002699
 [307] 0.1585709544 0.5840596777 0.9731478344 0.2473192735 0.7260147980 0.1306955793
 [313] 0.3365990470 0.6026880487 0.7368518653 0.8608391427 0.3992937202 0.7194843070
 [319] 0.6433569828 0.5424986043 0.9071187841 0.3282981367 0.7749076518 0.2165214381
 [325] 0.0472103735 0.0686018063 0.2765191752 0.3822418606 0.1694124391 0.6469293692
 [331] 0.9436365972 0.3072471754 0.3176196602 0.1467007769 0.3352298602 0.6350093334
 [337] 0.9951764157 0.6582820641 0.7899667609 0.1660851625 0.4239502897 0.8548106972
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 [349] 0.1518187974 0.8198987036 0.4862057963 0.4773776391 0.4175840786 0.0758781776
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 [367] 0.7300066468 0.7335145346 0.0792972863 0.5807187984 0.5156457808 0.2570263697
 [373] 0.1504795854 0.5727218324 0.0128178080 0.0834251611 0.0209616871 0.6992938285
 [379] 0.2282688480 0.6822454096 0.0238838394 0.2887757747 0.1463635425 0.9152544559
 [385] 0.6804073501 0.4592283898 0.3975719618 0.0802665085 0.3367372374 0.6099104005
 [391] 0.0356710482 0.4916866677 0.0047933413 0.1545117245 0.2712159552 0.5918083409
 [397] 0.7171341462 0.6990721696 0.8271869901 0.2398257348 0.5072801582 0.6058018233
 [403] 0.8914151028 0.4923852233 0.2223620117 0.9429353350 0.6040356096 0.7675695225
 [409] 0.7451263672 0.5992128771 0.9068828521 0.2341163356 0.0969357057 0.1929956255
 [415] 0.5982973095 0.6890373835 0.0413237763 0.0850601107 0.1649980567 0.1658545597
 [421] 0.4947599854 0.1823289975 0.6284685055 0.4547470761 0.0953595104 0.0140581705
 [427] 0.4907874327 0.2105436187 0.2493438134 0.5534053912 0.2169283482 0.2793459563
 [433] 0.0261253777 0.2323151339 0.7125301543 0.5020022376 0.8034931759 0.0924098216
 [439] 0.1615061166 0.9027890298 0.2281975031 0.9786748399 0.7340596333 0.4956357144
 [445] 0.7242103612 0.5356010623 0.1134939490 0.4895461098 0.4796019825 0.6501526767
 [451] 0.3329440088 0.6820555478 0.7463785849 0.4896086277 0.1034217919 0.5058697300
 [457] 0.3221743821 0.8195161736 0.0838605486 0.3873666088 0.2941668163 0.4260144472
 [463] 0.3350578891 0.3258924146 0.3916206173 0.2009362340 0.8239885989 0.3265720809
 [469] 0.9282071420 0.9763367543 0.0379182116 0.4339882472 0.2374774964 0.3476610285
 [475] 0.5472827295 0.4003619324 0.7624940664 0.9116519518 0.0040850199 0.7330996718
 [481] 0.1740127272 0.8049717620 0.0075717392 0.9771125570 0.7955783243 0.0144118696
 [487] 0.8963649918 0.2342139954 0.5608912654 0.0836209142 0.3466925544 0.1487231416
 [493] 0.4845011646 0.8623096385 0.4493532485 0.5351355618 0.2195945857 0.3571615899
 [499] 0.8581730621 0.9093055232 0.2139085567 0.2804734318 0.3649656270 0.4088750313
 [505] 0.3285696985 0.1094897319 0.3632333986 0.9674293610 0.7779444415 0.3823949543
 [511] 0.1248737345 0.2495010345 0.1504461294 0.9437239342 0.1148358145 0.6831774554
 [517] 0.1537112457 0.7770048517 0.6501947089 0.3385859511 0.4467842233 0.7614047679
 [523] 0.9788927381 0.4254189291 0.3716121314 0.9696653774 0.7961666071 0.6239288945
 [529] 0.8445308533 0.2053020163 0.6924139890 0.6617230728 0.4148488333 0.4707834348
 [535] 0.4124958636 0.8810476793 0.1146447732 0.8451233554 0.7220749810 0.1844010277
 [541] 0.7237719917 0.4407533162 0.9911362406 0.5436269507 0.9878384178 0.1546489392
 [547] 0.5684602411 0.2770691913 0.0304270341 0.0949413135 0.1252263518 0.2543344464
 [553] 0.0242328119 0.1694781709 0.7333341003 0.0514725875 0.5873936814 0.1170166207
 [559] 0.2067274616 0.0326327525 0.3798442389 0.0022155935 0.8750128628 0.7652705114
 [565] 0.6390411472 0.4948728761 0.7838380860 0.0630609333 0.9706285311 0.3147732329
 [571] 0.3290663662 0.7357689580 0.4901103555 0.0191485604 0.2139829330 0.5350206789
 [577] 0.4278836758 0.6077378684 0.1216959753 0.9861383943 0.0325329019 0.7432355129
 [583] 0.0060374900 0.9704808788 0.7713513542 0.4900721120 0.8065290819 0.1572252622
 [589] 0.6443131424 0.3512870930 0.3560227686 0.6526311455 0.3721986701 0.4031456402
 [595] 0.8221534071 0.6785364273 0.9812777884 0.7608353266 0.7124382934 0.5034039827
 [601] 0.1192475944 0.6832990660 0.2713052264 0.8941503267 0.9996054855 0.9184879544
 [607] 0.1362441695 0.0002251077 0.1789711670 0.9893231063 0.7047664153 0.6168097631
 [613] 0.5176912268 0.2018628680 0.2997456630 0.7290343778 0.6717546638 0.4207585588
 [619] 0.1954545727 0.3259694258 0.7420423650 0.1717134843 0.6724284951 0.4713584960
 [625] 0.4474110539 0.4179515556 0.2477565006 0.6578478760 0.6924080905 0.1668296218
 [631] 0.7746304890 0.5942663320 0.1317527073 0.2035751657 0.3882659607 0.4187444590
 [637] 0.6909204766 0.5544542237 0.5283430865 0.2734285831 0.2426345155 0.8979874914
 [643] 0.9456685386 0.1625117232 0.5967412968 0.4394169238 0.3514434577 0.7905629108
 [649] 0.7392298994 0.3620085472 0.2034537867 0.2429930873 0.4251041504 0.0586098147
 [655] 0.0162772670 0.1424067376 0.6508282481 0.5460378341 0.7507324600 0.8996480496
 [661] 0.1414941883 0.9808185552 0.1030123316 0.8371269450 0.5198281720 0.7366203118
 [667] 0.9213139356 0.6461560461 0.6408564301 0.1288137905 0.2699930616 0.6220669324
 [673] 0.7665831617 0.1210450201 0.2232946809 0.3798300289 0.5846862787 0.5189567394
 [679] 0.9162212736 0.4093876612 0.1861332307 0.9632251881 0.3937999737 0.0813518267
 [685] 0.7497915731 0.6084378957 0.4862000181 0.8380167542 0.7425894279 0.5994733126
 [691] 0.6688746992 0.9539705101 0.8768815396 0.7869694464 0.3860523341 0.8190679346
 [697] 0.5646244782 0.4589648513 0.4647693558 0.2913056614 0.1262858009 0.2017287080
 [703] 0.2809974356 0.5484609343 0.7523124583 0.9414127472 0.0145921718 0.5153684692
 [709] 0.1110372181 0.1216593301 0.3269471949 0.1438852355 0.2164425285 0.6674280544
 [715] 0.0469519012 0.5784755507 0.3969794483 0.7243092822 0.0760252145 0.7285087813
 [721] 0.5041034673 0.1210853340 0.4484741807 0.0240510109 0.5335889926 0.4434078613
 [727] 0.9795974745 0.3201692471 0.7112002015 0.2342665849 0.8791463870 0.1839046078
 [733] 0.5470634247 0.1566809641 0.3345688628 0.1318995591 0.3307294193 0.0470857869
 [739] 0.0665271006 0.1440576533 0.1249329238 0.3985288361 0.0400669071 0.2611601190
 [745] 0.1767847943 0.1187570240 0.4317503454 0.7080162047 0.8867361255 0.8935594361
 [751] 0.8563931580 0.8996844138 0.6390994933 0.1356954109 0.3046495826 0.4939068517
 [757] 0.1487088385 0.2412804149 0.8620909246 0.5930595915 0.2901539077 0.0542861163
 [763] 0.8220729233 0.0714245077 0.6134535465 0.5308489848 0.3542374686 0.9828395795
 [769] 0.6369439113 0.6418908593 0.3598983479 0.3923579400 0.7890554870 0.6517297322
 [775] 0.7090366518 0.3926864977 0.7396019232 0.0626555988 0.6862657436 0.9879005835
 [781] 0.9118490499 0.8423305134 0.7005184462 0.9267070860 0.7837952606 0.1025600195
 [787] 0.1182253632 0.7594873386 0.6024686847 0.5859231293 0.9532111034 0.9850385554
 [793] 0.4954742196 0.8277183607 0.2884248045 0.7309494309 0.7616153711 0.1611983286
 [799] 0.1017391792 0.8850532425 0.2413687664 0.1014970876 0.4007094105 0.8941373509
 [805] 0.2215914253 0.3062322864 0.7905161380 0.5466428674 0.2785848643 0.2652979598
 [811] 0.5115487069 0.9819219009 0.0701374703 0.9535987588 0.6933154187 0.4778489555
 [817] 0.9997382550 0.9729567812 0.2228214422 0.8450444836 0.8686264105 0.8468802635
 [823] 0.6080730669 0.3812184821 0.5776773776 0.8944509035 0.7766336437 0.2548160411
 [829] 0.7335720707 0.4255651560 0.2068432852 0.8420366950 0.0774750921 0.4688193244
 [835] 0.0643412214 0.5932301394 0.6010031393 0.8377875146 0.4511966832 0.4300334690
 [841] 0.5160218071 0.3849363964 0.4023754830 0.7245898240 0.7321459524 0.8692007834
 [847] 0.7272457056 0.4413125716 0.8329250502 0.6807133428 0.1591848259 0.0542618036
 [853] 0.8913943467 0.2373613883 0.8317293305 0.8694940622 0.0294361996 0.3181751307
 [859] 0.4764996430 0.7187043441 0.5477083029 0.4625368720 0.5962782747 0.6126994678
 [865] 0.6221474073 0.6371247778 0.1640210709 0.7045558227 0.5255900852 0.5965440346
 [871] 0.6770375895 0.9766209478 0.3820382696 0.6742222943 0.7460294933 0.3491613068
 [877] 0.0919970299 0.0637004381 0.4751784945 0.2877281881 0.7560904960 0.4588858259
 [883] 0.7334802255 0.1471474306 0.3374052039 0.5656598579 0.7584063499 0.8064020269
 [889] 0.3580035994 0.3860293321 0.7494183314 0.5212320825 0.4174708871 0.1340618635
 [895] 0.9936177477 0.6886814099 0.5068169987 0.5814102777 0.4242308887 0.5639884323
 [901] 0.8830139312 0.0982959750 0.2146654785 0.6807243441 0.1628050890 0.7572268016
 [907] 0.1964294632 0.3425076674 0.8770330847 0.4377513744 0.9724188237 0.6881354057
 [913] 0.3753761694 0.5121879278 0.5138458398 0.9902386899 0.5734816088 0.6065002880
 [919] 0.9951917783 0.1275034364 0.0890329692 0.1891442206 0.7124165062 0.2074178842
 [925] 0.9302047448 0.6464639226 0.5989346753 0.8518734540 0.8396230648 0.3592102227
 [931] 0.6794665814 0.2785662606 0.2294404446 0.2142935827 0.5122396513 0.5641451801
 [937] 0.2871123233 0.6544291917 0.3599313958 0.2806923210 0.3154335284 0.1257613638
 [943] 0.5861179193 0.5841497506 0.3889641498 0.5429467149 0.2128717258 0.5083257527
 [949] 0.0951871233 0.6856456779 0.6887263348 0.1640374223 0.5396259181 0.7093687954
 [955] 0.9449228828 0.9260892239 0.4904331779 0.8694419897 0.4165218226 0.6200187324
 [961] 0.4621735991 0.3656709479 0.8293851566 0.1351392185 0.2658876595 0.0011468860
 [967] 0.7336906190 0.7248770467 0.5112785922 0.3536684538 0.3227688875 0.7581872005
 [973] 0.4305685751 0.8908450758 0.6365275803 0.2179575394 0.2394035361 0.5602735409
 [979] 0.1962844776 0.2525159312 0.6641522519 0.5191365703 0.1010169635 0.0727637846
 [985] 0.3662000836 0.9285115255 0.1875288558 0.1630475457 0.0146506080 0.4642939259
 [991] 0.0351050105 0.2865314928 0.1963470026 0.8583061239 0.8657710886 0.8433983244
 [997] 0.6840511428 0.7422371330 0.1278159269 0.9205467060
 [ reached getOption("max.print") -- omitted 11897 entries ]

$adjpval
   [1] 0.9276450 0.9276450 0.9548246 0.9548079 0.9739354 0.9776476 0.9276450 0.9852988 0.9578653
  [10] 0.9405526 0.9701563 0.8036151 0.9910125 0.9865384 0.9852988 0.9776476 0.9893372 0.9578653
  [19] 0.9776476 0.7524920 0.9889433 0.9776476 0.9776476 0.9991188 0.9739354 0.9832951 0.9776476
  [28] 0.9276450 0.9471070 0.9578653 0.9425390 0.9194714 0.9211834 0.8323740 0.9471070 0.9776476
  [37] 0.9739354 0.9211834 0.9256901 0.9578653 0.9776476 0.9400112 0.9231483 0.9211834 0.9276450
  [46] 0.9759312 0.9363420 0.9776476 0.9776476 0.9548079 0.9939550 0.9504501 0.9920224 0.9832951
  [55] 0.9270488 0.9776476 0.9211834 0.9211834 0.9695358 0.9776476 0.9739354 0.9901160 0.9700968
  [64] 0.9815507 0.9776476 0.9211834 0.9595363 0.9211834 0.9958783 0.9276450 0.9211834 0.9211834
  [73] 0.9759312 0.9776476 0.9211834 0.9578653 0.9870438 0.9211834 0.9765234 0.9776476 0.9578653
  [82] 0.9721427 0.7892420 0.9557346 0.9862830 0.9798382 0.9875395 0.9893372 0.9716805 0.9471070
  [91] 0.9501569 0.9893372 0.9928269 0.9942453 0.9578653 0.9739354 0.9894996 0.9904060 0.9276450
 [100] 0.8208171 0.9194714 0.9919692 0.9276450 0.9776476 0.9578653 0.9920224 0.9716805 0.9471070
 [109] 0.9548079 0.9776476 0.9776476 0.9211834 0.9919475 0.9865384 0.9739354 0.9276450 0.9321802
 [118] 0.9471070 0.9776476 0.9858777 0.9425390 0.9845562 0.9858777 0.9994451 0.9606312 0.9739354
 [127] 0.9211834 0.9959875 0.9776476 0.9578653 0.9471070 0.9901160 0.9276450 0.9584361 0.9276450
 [136] 0.9716419 0.9716419 0.8036151 0.9759312 0.9500315 0.9739354 0.9749151 0.9861968 0.9578653
 [145] 0.9471070 0.8036151 0.9911129 0.9983855 0.9211834 0.9548079 0.9256901 0.9211834 0.9211834
 [154] 0.9899885 0.9297893 0.9939550 0.9976186 0.9776476 0.9858777 0.9256901 0.9211834 0.9211834
 [163] 0.9739354 0.9776476 0.9211834 0.9776476 0.9858777 0.9466562 0.9471070 0.9211834 0.9548246
 [172] 0.9211834 0.9858777 0.9776476 0.9471070 0.9939550 0.9578653 0.9211834 0.9211834 0.9211834
 [181] 0.9776476 0.9211834 0.9211834 0.9417110 0.9776476 0.9471070 0.9776476 0.9276450 0.9920472
 [190] 0.9919475 0.9994451 0.9211834 0.9776476 0.9211834 0.9759312 0.9471070 0.9776476 0.9211834
 [199] 0.9371603 0.9276450 0.9471070 0.9425390 0.9700968 0.9776476 0.9776476 0.9932475 0.9231483
 [208] 0.9603451 0.9946473 0.9211834 0.9471070 0.9739354 0.9739354 0.9587765 0.9739354 0.9519806
 [217] 0.9618485 0.9276450 0.9211834 0.9894996 0.9858777 0.9211834 0.9739354 0.9371603 0.9919475
 [226] 0.9231483 0.9471070 0.9578653 0.9405526 0.9557346 0.9471070 0.9739354 0.9211834 0.9776476
 [235] 0.9728673 0.9276450 0.9471070 0.9776476 0.9578653 0.9471070 0.9776476 0.9765234 0.9276450
 [244] 0.9471070 0.9776476 0.9776476 0.9500315 0.9211834 0.9469569 0.9910125 0.9923433 0.9578653
 [253] 0.9716419 0.9757295 0.9211834 0.9776476 0.9471070 0.9765234 0.9845562 0.9700968 0.9548079
 [262] 0.9578653 0.9739354 0.9920472 0.9211834 0.9920472 0.9211834 0.9991188 0.8501345 0.8036151
 [271] 0.9276450 0.9578653 0.9983855 0.9776476 0.9231483 0.9994451 0.9759312 0.9276450 0.9276450
 [280] 0.9211834 0.9469249 0.9832951 0.9211834 0.9471070 0.9832951 0.9276450 0.9321802 0.9832951
 [289] 0.9739354 0.9776476 0.9858777 0.9716805 0.9759312 0.9587765 0.9920472 0.9749151 0.9211834
 [298] 0.9739354 0.9858777 0.9276450 0.9893372 0.9425390 0.9776476 0.9548079 0.9321802 0.9211834
 [307] 0.9231483 0.9739354 0.9982935 0.9299663 0.9776476 0.9211834 0.9471070 0.9740436 0.9776476
 [316] 0.9870438 0.9539146 0.9776476 0.9776476 0.9701563 0.9919475 0.9471070 0.9790309 0.9276450
 [325] 0.9211834 0.9211834 0.9371603 0.9471070 0.9243540 0.9776476 0.9939550 0.9458710 0.9471070
 [334] 0.9231483 0.9471070 0.9776476 0.9994451 0.9776476 0.9815690 0.9231483 0.9548079 0.9865384
 [343] 0.9959875 0.9489663 0.9776476 0.9211834 0.9276450 0.9510504 0.9231483 0.9855749 0.9578653
 [352] 0.9578653 0.9548079 0.9211834 0.9790309 0.9832951 0.9687699 0.9759312 0.9548079 0.9858777
 [361] 0.9276450 0.9861968 0.9901160 0.9231483 0.9211834 0.9776476 0.9776476 0.9776476 0.9211834
 [370] 0.9739354 0.9639698 0.9323014 0.9231483 0.9739354 0.8208171 0.9211834 0.8921239 0.9776476
 [379] 0.9276450 0.9776476 0.8921239 0.9405526 0.9231483 0.9919692 0.9776476 0.9578653 0.9533457
 [388] 0.9211834 0.9471070 0.9749151 0.9194714 0.9583320 0.8036151 0.9231483 0.9363420 0.9739354
 [397] 0.9776476 0.9776476 0.9858777 0.9276450 0.9618485 0.9749151 0.9896732 0.9584361 0.9276450
 [406] 0.9939550 0.9745118 0.9777967 0.9776476 0.9739354 0.9919475 0.9276450 0.9211834 0.9276450
 [415] 0.9739354 0.9776476 0.9211834 0.9211834 0.9231483 0.9231483 0.9587765 0.9256901 0.9765234
 [424] 0.9578653 0.9211834 0.8208171 0.9583320 0.9276450 0.9308724 0.9716805 0.9276450 0.9374772
 [433] 0.8937374 0.9276450 0.9776476 0.9615470 0.9832951 0.9211834 0.9231483 0.9911129 0.9276450
 [442] 0.9988896 0.9776476 0.9587765 0.9776476 0.9695358 0.9211834 0.9578653 0.9578653 0.9776476
 [451] 0.9471070 0.9776476 0.9776476 0.9578653 0.9211834 0.9618485 0.9471070 0.9852988 0.9211834
 [460] 0.9489663 0.9425390 0.9548079 0.9471070 0.9471070 0.9500315 0.9276450 0.9858777 0.9471070
 [469] 0.9920472 0.9986050 0.9194714 0.9548246 0.9276450 0.9471070 0.9716419 0.9539146 0.9776476
 [478] 0.9919475 0.8036151 0.9776476 0.9256901 0.9832951 0.8036151 0.9988726 0.9826924 0.8323740
 [487] 0.9901160 0.9276450 0.9739354 0.9211834 0.9471070 0.9231483 0.9578653 0.9871023 0.9578653
 [496] 0.9692737 0.9276450 0.9471070 0.9869149 0.9919475 0.9276450 0.9375339 0.9471070 0.9548079
 [505] 0.9471070 0.9211834 0.9471070 0.9975963 0.9794740 0.9471070 0.9211834 0.9308724 0.9231483
 [514] 0.9939550 0.9211834 0.9776476 0.9231483 0.9794740 0.9776476 0.9471070 0.9578653 0.9776476
 [523] 0.9989539 0.9548079 0.9471070 0.9975985 0.9826924 0.9759312 0.9858777 0.9276450 0.9776476
 [532] 0.9776476 0.9548079 0.9578653 0.9548079 0.9893372 0.9211834 0.9860282 0.9776476 0.9264070
 [541] 0.9776476 0.9570663 0.9994451 0.9707022 0.9994451 0.9231483 0.9739354 0.9371603 0.9143439
 [550] 0.9211834 0.9211834 0.9321802 0.8921239 0.9243540 0.9776476 0.9211834 0.9739354 0.9211834
 [559] 0.9276450 0.9194714 0.9471070 0.7239064 0.9892987 0.9776476 0.9776476 0.9587765 0.9810947
 [568] 0.9211834 0.9976186 0.9471070 0.9471070 0.9776476 0.9578653 0.8889947 0.9276450 0.9692737
 [577] 0.9548079 0.9749151 0.9211834 0.9991188 0.9194714 0.9776476 0.8036151 0.9976186 0.9787961
 [586] 0.9578653 0.9832951 0.9231483 0.9776476 0.9471070 0.9471070 0.9776476 0.9471070 0.9541878
 [595] 0.9858777 0.9776476 0.9991188 0.9776476 0.9776476 0.9618485 0.9211834 0.9776476 0.9363420
 [604] 0.9899885 0.9998397 0.9920224 0.9211834 0.4165295 0.9256901 0.9994451 0.9776476 0.9759312
 [613] 0.9639874 0.9276450 0.9429875 0.9776476 0.9776476 0.9548079 0.9276450 0.9471070 0.9776476
 [622] 0.9256901 0.9776476 0.9578653 0.9578653 0.9548079 0.9303398 0.9776476 0.9776476 0.9231483
 [631] 0.9790309 0.9739354 0.9211834 0.9276450 0.9492827 0.9548079 0.9776476 0.9716805 0.9668655
 [640] 0.9371603 0.9276450 0.9901970 0.9942091 0.9231483 0.9739354 0.9557346 0.9471070 0.9818846
 [649] 0.9776476 0.9471070 0.9276450 0.9276450 0.9548079 0.9211834 0.8548071 0.9211834 0.9776476
 [658] 0.9716419 0.9776476 0.9907952 0.9211834 0.9991188 0.9211834 0.9858777 0.9643590 0.9776476
 [667] 0.9920472 0.9776476 0.9776476 0.9211834 0.9363420 0.9759312 0.9777967 0.9211834 0.9276450
 [676] 0.9471070 0.9739354 0.9639874 0.9919692 0.9548079 0.9276450 0.9966488 0.9504501 0.9211834
 [685] 0.9776476 0.9749151 0.9578653 0.9858777 0.9776476 0.9739354 0.9776476 0.9955784 0.9893372
 [694] 0.9815507 0.9483651 0.9852988 0.9739354 0.9578653 0.9578653 0.9413603 0.9211834 0.9276450
 [703] 0.9375339 0.9716419 0.9776476 0.9939550 0.8323740 0.9639698 0.9211834 0.9211834 0.9471070
 [712] 0.9211834 0.9276450 0.9776476 0.9211834 0.9739354 0.9529476 0.9776476 0.9211834 0.9776476
 [721] 0.9618485 0.9211834 0.9578653 0.8921239 0.9687699 0.9575450 0.9991188 0.9471070 0.9776476
 [730] 0.9276450 0.9893372 0.9264070 0.9716419 0.9231483 0.9471070 0.9211834 0.9471070 0.9211834
 [739] 0.9211834 0.9211834 0.9211834 0.9539146 0.9194714 0.9339651 0.9256901 0.9211834 0.9548079
 [748] 0.9776476 0.9893372 0.9899885 0.9865384 0.9907952 0.9776476 0.9211834 0.9447140 0.9587472
 [757] 0.9231483 0.9276450 0.9871023 0.9739354 0.9413207 0.9211834 0.9858777 0.9211834 0.9757295
 [766] 0.9676833 0.9471070 0.9991188 0.9776476 0.9776476 0.9471070 0.9500315 0.9815507 0.9776476
 [775] 0.9776476 0.9501569 0.9776476 0.9211834 0.9776476 0.9994451 0.9919475 0.9858777 0.9776476
 [784] 0.9920472 0.9810947 0.9211834 0.9211834 0.9776476 0.9739354 0.9739354 0.9955109 0.9991188
 [793] 0.9587765 0.9858777 0.9405526 0.9776476 0.9776476 0.9231483 0.9211834 0.9893372 0.9276450
 [802] 0.9211834 0.9539146 0.9899885 0.9276450 0.9455797 0.9818846 0.9716419 0.9371603 0.9339651
 [811] 0.9638328 0.9991188 0.9211834 0.9955273 0.9776476 0.9578653 0.9998397 0.9982935 0.9276450
 [820] 0.9860254 0.9885242 0.9862830 0.9749151 0.9471070 0.9739354 0.9899885 0.9794729 0.9321802
 [829] 0.9776476 0.9548079 0.9276450 0.9858777 0.9211834 0.9578653 0.9211834 0.9739354 0.9739354
 [838] 0.9858777 0.9578653 0.9548079 0.9639698 0.9479618 0.9541878 0.9776476 0.9776476 0.9885242
 [847] 0.9776476 0.9573616 0.9858777 0.9776476 0.9231483 0.9211834 0.9896732 0.9276450 0.9858777
 [856] 0.9885283 0.9143439 0.9471070 0.9578653 0.9776476 0.9716419 0.9578653 0.9739354 0.9757295
 [865] 0.9759312 0.9776476 0.9231483 0.9776476 0.9665227 0.9739354 0.9776476 0.9986110 0.9471070
 [874] 0.9776476 0.9776476 0.9471070 0.9211834 0.9211834 0.9578653 0.9405526 0.9776476 0.9578653
 [883] 0.9776476 0.9231483 0.9471070 0.9739354 0.9776476 0.9832951 0.9471070 0.9483651 0.9776476
 [892] 0.9643684 0.9548079 0.9211834 0.9994451 0.9776476 0.9618485 0.9739354 0.9548079 0.9739354
 [901] 0.9893372 0.9211834 0.9276450 0.9776476 0.9231483 0.9776476 0.9276450 0.9471070 0.9893372
 [910] 0.9557346 0.9982129 0.9776476 0.9471070 0.9638328 0.9638446 0.9994451 0.9739354 0.9749151
 [919] 0.9994451 0.9211834 0.9211834 0.9276450 0.9776476 0.9276450 0.9920472 0.9776476 0.9739354
 [928] 0.9865384 0.9858777 0.9471070 0.9776476 0.9371603 0.9276450 0.9276450 0.9638328 0.9739354
 [937] 0.9405526 0.9776476 0.9471070 0.9375339 0.9471070 0.9211834 0.9739354 0.9739354 0.9495213
 [946] 0.9705314 0.9276450 0.9618485 0.9211834 0.9776476 0.9776476 0.9231483 0.9700968 0.9776476
 [955] 0.9939550 0.9920472 0.9580700 0.9885283 0.9548079 0.9759312 0.9578653 0.9471070 0.9858777
 [964] 0.9211834 0.9339651 0.6431039 0.9776476 0.9776476 0.9638328 0.9471070 0.9471070 0.9776476
 [973] 0.9548079 0.9894996 0.9776476 0.9276450 0.9276450 0.9739354 0.9276450 0.9321802 0.9776476
 [982] 0.9639874 0.9211834 0.9211834 0.9471070 0.9920472 0.9276450 0.9231483 0.8323740 0.9578653
 [991] 0.9194714 0.9405526 0.9276450 0.9869149 0.9877574 0.9858777 0.9776476 0.9776476 0.9211834
[1000] 0.9920472
 [ reached getOption("max.print") -- omitted 11897 entries ]
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGlzdChpbnZub3JtY29tYiRyYXdwdmFsLCBicmVha3M9MTAwLCBjb2w9XCJncmV5XCIsIG1haW49XCJJbnZlcnNlIG5vcm1hbCBtZXRob2RcIiwgeGxhYiA9IFwiUmF3IHAtdmFsdWVzIEVjb3R5cGUgaW4gQ29udHJvbCAobWV0YS1hbmFseXNpcylcIilcbmBgYCJ9 -->

```r
hist(invnormcomb$rawpval, breaks=100, col="grey", main="Inverse normal method", xlab = "Raw p-values Ecotype in Control (meta-analysis)")
```

<!-- rnb-source-end -->

<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

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```r

## Summarize the results. DE are the results from the original analyses of the datasets individually.
EC_results_metastats <- data.frame(DE, "DE.fishercomb_adjP"=fishcomb$adjpval, "DE.invnorm_adj_P"=invnormcomb$adjpval)
library(data.table)
setDT(EC_results_metastats, keep.rownames = "Gene")  # make the rownames a part of the dataframe with column name "Gene"
head(EC_results_metastats)
```

<!-- rnb-source-end -->

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<!-- rnb-frame-end -->

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```r
EC_results <- data.frame(DE, "DE.fishercomb"=ifelse(fishcomb$adjpval<=0.05,1,0), "DE.invnorm"=ifelse(invnormcomb$adjpval<=0.05,1,0))
head(EC_results)
```

<!-- rnb-source-end -->

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```r

## Dealing with conflicts in the sign of differential expression 
signsFC <- mapply(EC_FC, FUN=function(x) sign(x))
sumsigns <- apply(signsFC,1,sum)
commonsngFC <- ifelse(abs(sumsigns)==dim(signsFC)[2], sign(sumsigns), 0)
unionDE <- unique(c(fishcomb$DEindices, invnormcomb$DEindicies))
FC.selecDE <- data.frame(EC_results[unionDE,], do.call(cbind,EC_FC) [unionDE,], signFC=commonsngFC[unionDE], DE=DE[unionDE])
  # keep the ones that don't have a conflict (1 or -1)
keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),]
conflictDE <- FC.selecDE[which(FC.selecDE$signFC==0),]
head(FC.selecDE)  # the ones with conflict = 0
```

<!-- rnb-source-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChrZWVwREUpXG5gYGAifQ== -->

```r
head(keepDE)
```

<!-- rnb-source-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChjb25mbGljdERFKVxuYGBgIn0= -->

```r
head(conflictDE)
```

<!-- rnb-source-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxudGFibGUoY29uZmxpY3RERSRERSlcbmBgYCJ9 -->

```r
table(conflictDE$DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiPCB0YWJsZSBvZiBleHRlbnQgMCA+XG4ifQ== -->

```
< table of extent 0 >
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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```r
### Merge results and write to  a file
setDT(FC.selecDE, keep.rownames = "Gene")
head(FC.selecDE)
```

<!-- rnb-source-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChFQ19yZXN1bHRzX21ldGFzdGF0cylcbmBgYCJ9 -->

```r
head(EC_results_metastats)
```

<!-- rnb-source-end -->

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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG4jIG1lcmdlIHRvcC50YWJsZXMgZnJvbSBIUzIwMDggYW5kIDIwMTIgLSBhbGwgZ2VuZXMgdGhhdCB3ZXJlIG5vdCBvcmlnaW5hbGx5IGZpbHRlcmVkIG90dS5cbmRpbSh0b3AudGFibGVfQ19FY290eXBlczA4KVxuYGBgIn0= -->

```r

# merge top.tables from HS2008 and 2012 - all genes that were not originally filtered otu.
dim(top.table_C_Ecotypes08)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEzMDY3ICAgICA3XG4ifQ== -->

```
[1] 13067     7
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGltKHRvcC50YWJsZV9DX0Vjb3R5cGVzMTIpXG5gYGAifQ== -->

```r
dim(top.table_C_Ecotypes12)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE0NDMxICAgICA3XG4ifQ== -->

```
[1] 14431     7
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxudG9wLnRhYmxlLkVDX0FsbDwtIG1lcmdlKHRvcC50YWJsZV9DX0Vjb3R5cGVzMDgsdG9wLnRhYmxlX0NfRWNvdHlwZXMxMixieT1cIkdlbmVcIixhbGwueCA9IFRSVUUsIGFsbC55PVRSVUUsIHN1ZmZpeGVzID0gYyhcIi4wOFwiLCBcIi4xMlwiKSlcbmRpbSh0b3AudGFibGUuRUNfQWxsKVxuYGBgIn0= -->

```r
top.table.EC_All<- merge(top.table_C_Ecotypes08,top.table_C_Ecotypes12,by="Gene",all.x = TRUE, all.y=TRUE, suffixes = c(".08", ".12"))
dim(top.table.EC_All)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDE0NjAxICAgIDEzXG4ifQ== -->

```
[1] 14601    13
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBtZXJnZSB3aXRoIG1ldGFuYWx5c2lzXG5cbkVjb3R5cGVDb250cm9sX3Jlc3VsdHMgPC0gbWVyZ2UodG9wLnRhYmxlLkVDX0FsbCxGQy5zZWxlY0RFLCBhbGwueCA9IFRSVUUpXG5FY290eXBlQ29udHJvbF9yZXN1bHRzMiA8LSBtZXJnZShFY290eXBlQ29udHJvbF9yZXN1bHRzLCBFQ19yZXN1bHRzX21ldGFzdGF0cywgYWxsLnggPSBUUlVFKVxuaGVhZChFY290eXBlQ29udHJvbF9yZXN1bHRzMilcbmBgYCJ9 -->

```r
# merge with metanalysis

EcotypeControl_results <- merge(top.table.EC_All,FC.selecDE, all.x = TRUE)
EcotypeControl_results2 <- merge(EcotypeControl_results, EC_results_metastats, all.x = TRUE)
head(EcotypeControl_results2)
```

<!-- rnb-source-end -->

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```r
write.table(EcotypeControl_results2, file = "OutputFiles/Meta_EcotypesControl.txt", row.names = F, sep = "\t", quote = F)
```

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### Make summary tables
Ecotype in Control

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyMgTWFrZSBhIHRhYmxlIG9mIHJlc3VsdHNcbmZpc2hjb21iX2RlIDwtIHJvd25hbWVzKGtlZXBERSlbd2hpY2goa2VlcERFWyxcIkRFLmZpc2hlcmNvbWJcIl09PTEpXVxuaW52bm9ybV9kZSA8LSByb3duYW1lcyhrZWVwREUpW3doaWNoKGtlZXBERVssXCJERS5pbnZub3JtXCJdPT0xKV1cbmluZHN0dWR5X2RlIDwtIGxpc3Qocm93bmFtZXMoa2VlcERFKVt3aGljaChrZWVwREVbLFwiREUuMjAwOERhdGFzZXRcIl09PTEpXSxyb3duYW1lcyhrZWVwREUpW3doaWNoKGtlZXBERVssXCJERS4yMDEyRGF0YXNldFwiXT09MSldKVxuSURELklSUihmaXNoY29tYl9kZSxpbmRzdHVkeV9kZSlcbmBgYCJ9 -->

```r
## Make a table of results
fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)]
invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)]
indstudy_de <- list(rownames(keepDE)[which(keepDE[,"DE.2008Dataset"]==1)],rownames(keepDE)[which(keepDE[,"DE.2012Dataset"]==1)])
IDD.IRR(fishcomb_de,indstudy_de)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICBERSAgSUREIExvc3MgIElEUiAgSVJSIFxuICAgMCAgICAwICAgIDAgIE5hTiAgTmFOIFxuIn0= -->

```
  DE  IDD Loss  IDR  IRR 
   0    0    0  NaN  NaN 
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuSURELklSUihpbnZub3JtX2RlLCBpbmRzdHVkeV9kZSlcbmBgYCJ9 -->

```r
IDD.IRR(invnorm_de, indstudy_de)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICBERSAgSUREIExvc3MgIElEUiAgSVJSIFxuICAgMCAgICAwICAgIDAgIE5hTiAgTmFOIFxuIn0= -->

```
  DE  IDD Loss  IDR  IRR 
   0    0    0  NaN  NaN 
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### Make Upset Plot, Ecotype in Control

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


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```r
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_EC08_top <- top.table_C_Ecotypes08[which(top.table_C_Ecotypes08$adj.P.Val<=0.05), ]
dim(UP_EC08_top)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAgN1xuIn0= -->

```
[1] 0 7
```



<!-- rnb-output-end -->

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```r
  # converting column of "Gene" in dataframe column into vector by passing as index
Ecotypes_Control_HS2008 = UP_EC08_top[['Gene']]
# Heat Treatment 2012
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_EC12_top <- top.table_C_Ecotypes12[which(top.table_C_Ecotypes12$adj.P.Val<=0.05), ]
dim(UP_EC12_top)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDAgN1xuIn0= -->

```
[1] 0 7
```



<!-- rnb-output-end -->

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```r
  #converting column of "Gene" in dataframe column into vector by passing as index
Ecotypes_Control_HS2012 = UP_EC12_top[['Gene']]
# MetaAnalysis
## Keep the genes from the meta-analysis that have invernorm adjusted p-value <= 0.05 and the log fold change is going in the same direction in both analyses
UP_MetaA <- EcotypeControl_results2[which(EcotypeControl_results2$DE.invnorm_adj_P<=0.05), ]
UP_MetaB <- UP_MetaA[which(abs(UP_MetaA$signFC)==1),]
dim(UP_MetaB)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdICAwIDIzXG4ifQ== -->

```
[1]  0 23
```



<!-- rnb-output-end -->

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```r
  #converting column of "Gene" in dataframe column into vector by passing as index
EcotypeControl_Meta = UP_MetaB[['Gene']]

list <- list(HS2008 = Ecotypes_Control_HS2008, HS2012 = Ecotypes_Control_HS2012, MetaAnalysis = EcotypeControl_Meta)

### No significant genes for the Interaction so don't need to make upset plot
# Make upset put and save as a .pdf file
#pdf("OutputGraphs/Upset_EcotypeControl.pdf")
  #make plot
#upset(fromList(list), mainbar.y.label = "Number of Genes Differentially expressed genes (adjusted p-value=0.05)", sets.x.label = "DEGs",order.by = "freq")
#dev.off()
```

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

Ecotype in Heat treatment

first get the data ready

#Merge top tables results for each dataset - keep the genes in common ~13000
library(plyr)
top.table_EH_intersect <- merge(top.table_H_Ecotypes08,top.table_H_Ecotypes12,by="Gene",all.x = FALSE, suffixes = c(".08", ".12"))
dim(top.table_EH_intersect)

[1] 12897    13

summary(top.table_EH_intersect)

     Gene              logFC.08         AveExpr.08          t.08           P.Value.08       
 Length:12897       Min.   :-8.3302   Min.   :-2.530   Min.   :-5.5505   Min.   :0.0000834  
 Class :character   1st Qu.:-0.6576   1st Qu.: 1.378   1st Qu.:-0.9672   1st Qu.:0.2409362  
 Mode  :character   Median :-0.1425   Median : 3.343   Median :-0.2759   Median :0.4890627  
                    Mean   :-0.3257   Mean   : 3.261   Mean   :-0.2704   Mean   :0.4940752  
                    3rd Qu.: 0.1918   3rd Qu.: 4.970   3rd Qu.: 0.4023   3rd Qu.:0.7445955  
                    Max.   : 8.2890   Max.   :15.915   Max.   : 4.7573   Max.   :0.9999750  
  adj.P.Val.08         B.08            logFC.12           AveExpr.12          t.12         
 Min.   :0.2709   Min.   :-6.2534   Min.   :-6.879790   Min.   :-5.122   Min.   :-5.48677  
 1st Qu.:0.9579   1st Qu.:-5.8831   1st Qu.:-0.148641   1st Qu.: 1.965   1st Qu.:-0.63216  
 Median :0.9776   Median :-5.3984   Median : 0.014519   Median : 3.609   Median : 0.06218  
 Mean   :0.9739   Mean   :-5.2880   Mean   : 0.008867   Mean   : 3.543   Mean   : 0.06131  
 3rd Qu.:0.9913   3rd Qu.:-4.9105   3rd Qu.: 0.181807   3rd Qu.: 5.057   3rd Qu.: 0.74689  
 Max.   :1.0000   Max.   : 0.3553   Max.   : 6.153036   Max.   :16.730   Max.   : 4.68476  
   P.Value.12         adj.P.Val.12         B.12       
 Min.   :0.0000264   Min.   :0.3803   Min.   :-6.061  
 1st Qu.:0.2493116   1st Qu.:0.9830   1st Qu.:-5.854  
 Median :0.4965019   Median :0.9896   Median :-5.543  
 Mean   :0.5005941   Mean   :0.9837   Mean   :-5.335  
 3rd Qu.:0.7522969   3rd Qu.:0.9991   3rd Qu.:-5.069  
 Max.   :0.9998897   Max.   :1.0000   Max.   : 2.609  

head(top.table_EH_intersect)

#Put p-value and Fold changes results in lists
EH_rawpval <- list("pvalue_08"=top.table_EH_intersect[["P.Value.08"]], "pvalue_12"=top.table_EH_intersect[["P.Value.12"]])
summary(EH_rawpval)

          Length Class  Mode   
pvalue_08 12897  -none- numeric
pvalue_12 12897  -none- numeric

EH_adjpval <- list("adjpvalue_08"=top.table_EH_intersect[["adj.P.Val.08"]], "adjpvalue_12"=top.table_EH_intersect[["adj.P.Val.12"]])
summary(EH_adjpval)

             Length Class  Mode   
adjpvalue_08 12897  -none- numeric
adjpvalue_12 12897  -none- numeric

EH_FC <- list("EH_FC_08"=top.table_EH_intersect[["logFC.08"]], "EH_FC_12"=top.table_EH_intersect[["logFC.12"]])
summary(EH_FC)

         Length Class  Mode   
EH_FC_08 12897  -none- numeric
EH_FC_12 12897  -none- numeric

# make matrix of 1 for genes DE and 0 for not.
DE<- mapply(EH_adjpval, FUN=function(x) ifelse(x <= 0.05, 1, 0))
studies <-c("2008Dataset", "2012Dataset")
colnames(DE)=paste("DE", studies,sep=".")
rownames(DE)=paste(top.table_EH_intersect$"Gene")
#DE<-cbind(top.table_H_intersect$"Gene", DE)
summary(DE)

 DE.2008Dataset DE.2012Dataset
 Min.   :0      Min.   :0     
 1st Qu.:0      1st Qu.:0     
 Median :0      Median :0     
 Mean   :0      Mean   :0     
 3rd Qu.:0      3rd Qu.:0     
 Max.   :0      Max.   :0     

head(DE)

      DE.2008Dataset DE.2012Dataset
A1CF               0              0
AAAS               0              0
AACS               0              0
AADAT              0              0
AAGAB              0              0
AAK1               0              0

dim(DE)

[1] 12897     2

#Check for normal distributions
par(mfrow = c(1,2))
hist(EH_rawpval[[1]], breaks=100, col="grey", main="2008 Dataset", xlab="Raw p-values")
hist(EH_rawpval[[2]], breaks=100, col="grey", main="2012 Dataset", xlab="Raw p-values")

Calculate the meta-analysis p-value

Ecotype in Heat

library(metaRNASeq)
# P-value combination using Fisher method 
fishcomb <- fishercomb(EH_rawpval, BHth = 0.05)
summary(fishcomb)

              Length Class  Mode   
DEindices         0  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric

hist(fishcomb$rawpval, breaks=100, col="grey", main="Fisher Method", xlab = "Raw p-values Ecotype in Heat (meta-analysis)")

# P-value combination using inverse nomral method method 
invnormcomb <- invnorm(EH_rawpval, nrep=c(12,20), BHth=0.05)
summary(invnormcomb)

              Length Class  Mode   
DEindices         0  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric

head(invnormcomb)

$DEindices
integer(0)

$TestStatistic
   [1]  2.158769127 -0.444155180 -0.644855070  1.965924000 -0.965689820 -1.367150656
   [7] -0.625385736 -0.237348342 -1.033861614  1.911587920  0.893186362 -0.403134504
  [13] -1.577839661 -0.551177939  0.240843667  0.410614216 -1.910781111 -2.246970696
  [19] -2.405247313  0.298902161 -0.193336061 -0.503620707 -1.111196372 -1.158094485
  [25]  1.399564291  1.480033569  1.128937280 -0.686038883  1.176329204  0.802012678
  [31] -0.281450290 -1.326294051  0.637784562  1.326289901  0.333636754 -1.043274898
  [37]  0.062040094 -0.836801510 -0.215599996  0.600346860  1.365228750  0.641994213
  [43]  0.454931151  2.093457913  0.467558745  0.439557637  1.259203898 -0.680182976
  [49] -1.292168795 -0.179278843  0.137091717 -0.292158122 -0.076479099 -1.684739397
  [55] -0.157736986 -0.096495202 -0.525339696 -1.753438564  1.763503125  1.267504074
  [61] -0.869821721  0.139629899  1.081446999  1.036505631 -0.815411956  0.697401571
  [67] -1.672265084 -0.631197540  1.928906025  1.530203994  1.424569984 -0.466844889
  [73]  0.503088365  0.332079379  1.473249162  1.149887538  0.462356983 -0.405443525
  [79] -1.100321332 -0.340914908 -0.376923135 -0.072651883 -0.398884411  0.124137542
  [85] -1.179687346  0.134663172 -1.012913258  0.385655529 -0.183547756  0.698687249
  [91]  0.609105060  1.679134689  1.531346714  2.065559216  0.202095222 -0.212547268
  [97] -0.273895088 -0.724627768 -1.140125279  0.174836574  1.350407492  0.803242631
 [103]  1.152313227 -1.073963419  1.527910441  2.037633517  0.709564468  0.458390568
 [109] -1.795458576 -0.824116250  1.481593239  1.032777242 -1.038989478 -0.666692014
 [115] -0.697884632  0.512361566  0.326150793  1.247010485  0.077685047  0.142268186
 [121] -0.456868085 -0.501620264  1.352538333 -0.152306172 -1.069347895 -0.082506953
 [127]  1.324003584 -0.090938998 -0.319107204 -2.225629279 -1.710114435 -0.599965571
 [133]  0.269310822  0.197788621 -0.204421686  0.977803364  0.632948151 -0.736015220
 [139] -1.114484282 -0.601256081  0.658204966 -1.475323437  0.655594802  0.980444999
 [145] -0.879889140 -0.160684970 -0.280521902  0.766539823  1.206466488  1.905474881
 [151] -1.666564979 -0.699190778 -0.141364585  0.001689606  0.202940861  2.328386900
 [157]  1.466694245  0.652622449 -0.531270827  1.014524219  0.504868682  1.432216400
 [163]  0.682466932  0.290512885  1.300471050  1.632199782  0.170671455 -0.499188997
 [169]  1.070893569  0.169627124  0.692749238  0.905503048 -1.017357345  0.107456110
 [175] -0.285069352 -2.176470097  1.028474381  0.127802140 -1.804384349 -1.434878661
 [181] -1.699013012 -0.979613259 -1.069558703  0.686271917  0.238427091 -0.033526081
 [187]  0.141412626  0.029483490 -0.019449002 -0.970633896  1.086986069 -0.127279141
 [193]  0.747647503  0.723564814  0.818608929 -1.349016174 -0.331677927  1.354776964
 [199] -0.280363868  0.690833010 -1.078389070  0.272645057 -0.432695919  0.910292660
 [205]  1.449252683  0.780552236  0.197512183  1.316895831  0.174795761 -0.696604838
 [211] -1.483276218  0.937063632  0.536670902  0.001472118  0.018344661 -0.400046436
 [217] -0.423803201  0.341251479  0.165276275 -1.305862000  0.085548215  0.540745613
 [223] -0.676654550 -0.363110929 -1.104438357  1.055470204 -2.062826398  0.116221808
 [229] -0.742468112  0.672336551 -1.578513125  0.487602538 -1.869916062  0.752223143
 [235] -0.161125279 -0.325347410  0.456804837 -0.117339467 -1.019710993 -1.381726427
 [241]  0.251638360  2.467771770  1.091585825 -1.409812380  0.017896175  0.316147499
 [247] -1.911376353 -0.851453246  1.554337207  1.434562229  0.274764111 -2.210448729
 [253]  0.766857048  1.156753479  0.760581184  0.418142336 -1.700846033 -0.475098588
 [259]  0.393172183  0.212176467 -0.357895984 -0.691230615 -1.283292354  0.037729168
 [265] -0.787805066 -0.932056667 -1.549727470  0.767928395  0.103143285  1.814517412
 [271] -1.147001858 -0.857947909 -1.277223560 -0.322781450 -0.718652458 -2.358744800
 [277]  0.335485345 -0.712679092 -0.713478979 -0.464724322 -0.983291221 -0.062232977
 [283]  1.369220840 -0.554936789  0.518681247  0.636885573 -0.348481800 -0.240775586
 [289]  0.040442734  1.403794873 -0.013974046  0.494465160  1.218188594  0.721090801
 [295]  0.016712690 -0.658482952 -2.130728572 -0.257842520  1.112635595 -1.769458061
 [301] -0.648805627 -0.581957680 -0.220944677 -0.887044440 -1.426972426  0.001388815
 [307]  0.533524276 -0.124507530 -0.419097987 -0.724543319  0.069479495  0.487806249
 [313]  1.695744974  2.090599861  0.083903608  1.637305245  0.900358043  0.559370876
 [319] -0.771085655 -0.239094618 -0.989335332 -1.067306044  1.125218223 -2.143408436
 [325]  0.184341929 -0.992980483 -0.905622374 -0.542727864 -0.223139050 -1.252905533
 [331]  0.898853412  0.517688754  0.849791441 -1.238184694  1.430354650  2.111057009
 [337]  0.619593317  1.063310947 -0.344860499 -1.082722042  0.775595219  0.176331279
 [343] -0.478726719  0.783683297  0.837556022  3.118274787  1.162915762  0.329126671
 [349] -0.162286891  1.814062288 -0.278390138  0.231815970 -0.121287605 -1.399217536
 [355] -1.092524583  1.176344811 -1.534527554  0.435634087  0.648836653 -1.009509528
 [361]  1.115470502 -2.100694687  1.157133204  0.967015451  0.640853503 -0.457395610
 [367]  1.341892464  0.924209580 -1.233384976  0.546973167  1.302646973  1.223252124
 [373]  0.522766082  1.028069490  0.232255291  0.019715059 -0.317494903  0.604118439
 [379]  0.734321631 -0.088840156  0.031817470 -2.139211832 -0.952551916 -1.648904827
 [385]  2.255615852  0.491199469 -0.428665410  0.090250839 -1.920868713 -0.449521697
 [391] -1.266454352 -0.074726992  1.176856072 -0.728843754 -1.029110018  0.468069154
 [397]  0.217443133  1.396493500 -0.405111609  0.014646657 -0.033077231  0.287106501
 [403] -0.558173905  0.884537683  0.002136958 -1.814620050 -0.595239190  0.194378475
 [409] -0.400957147  0.662041599 -0.424603559  0.238237750 -1.315256214 -0.985980420
 [415]  1.610265588 -1.106663545  1.191369714 -0.839687787 -0.082867250 -0.095753806
 [421]  0.718909853 -2.517183980 -1.155821217 -0.123365737 -0.372172969  2.003930958
 [427]  0.169446041  0.969515109  0.990815956 -0.362994486 -0.411062606  0.998356120
 [433]  0.997193668 -0.016169405  0.582585355 -1.387093863 -1.602605531  1.271254100
 [439]  0.720383911  1.065940800  0.205835535 -1.325132493 -0.256471789  1.289464430
 [445]  0.730081288 -0.844510068  0.507544164  0.948816340 -0.727280853  0.686001337
 [451] -1.795471671 -0.443674749 -0.887385032  0.399527220  1.471452812  1.179653117
 [457] -1.646356903  1.616374205 -0.652866212 -1.016393033  0.778414639 -0.290120242
 [463] -0.234503818 -0.122055173  0.541641656 -0.158247304  0.837901729  1.640391428
 [469]  1.397437361 -0.206526231  0.293694106  0.777080031  0.090174333  0.740270029
 [475] -1.880631446 -0.115723163  0.628712467  0.747411659  0.463728797 -0.060348745
 [481] -1.000819472 -0.812266079 -2.341122648 -0.160461296  0.650700548  1.756929143
 [487]  0.440104586 -0.630049643 -2.111045073  1.443964841  0.170718080  0.561825438
 [493]  0.208471410  0.344954630 -0.486816707  1.064129355  1.190375469  0.456759313
 [499] -2.066128636 -1.235790972 -0.785426398 -0.286541614 -2.076731818  0.132225862
 [505] -0.561304971 -0.558259620 -0.069979386 -1.066503325 -0.123136923 -1.201322982
 [511]  0.951554241 -0.267803325 -0.358784497  0.907067693 -1.357050368 -1.400393851
 [517]  0.364820791 -0.900778428 -0.717194848  0.036984844 -0.579378185 -0.173025141
 [523] -0.163199195 -0.121117500 -0.372771542  0.403245072  1.505351753  0.080665233
 [529]  0.027344295 -0.667469501 -0.296011787 -2.001924957 -0.260568001  1.186613556
 [535] -1.399408599 -1.501332112  1.244552530 -0.055943674  0.632518066 -0.483620722
 [541]  0.683925350 -0.832222361 -0.183107052 -0.047916586  1.278639647  0.745171158
 [547] -0.383736091 -0.257357975  0.783649922  0.595741534  0.669420401  1.470355381
 [553] -0.481248254 -0.350927372 -0.821860993  0.260666524  0.128193534  0.978994905
 [559]  0.764466298 -0.765591551  0.790275514  1.156662433  0.855481608  0.515879602
 [565] -1.311298450  0.367409233 -0.616481214  1.408471682  0.774576281  1.911274689
 [571] -0.728812963 -0.933071959  1.449639098  0.228784019  1.212288434  0.529027151
 [577]  0.411893615  1.513787139 -1.433320561  0.191192601  1.148432960  1.768078908
 [583]  3.646615914  0.812094615 -0.615793580  0.831109728 -0.712776011  0.375864469
 [589] -0.083891852 -0.977748850 -1.112927296 -1.617609891  1.822414854 -0.125221268
 [595]  0.125795174 -0.057892680 -1.883458413 -0.493154561  1.928179740 -0.110892796
 [601] -0.703460986  1.078208532  0.367980605 -0.757984136 -0.787052725  1.149928542
 [607]  1.739404542  1.615246936  1.595473801 -0.408571021  0.867143353  0.089809246
 [613]  0.836865287 -0.301526493  0.407610165 -0.073920323 -0.946780750 -0.479913927
 [619] -0.121487445 -0.532580467  0.409891754  0.023325082 -1.203151864 -2.494982251
 [625]  1.892971485 -0.441733014  0.423758692  1.208469216  1.543545058 -1.193729049
 [631] -1.568946963  0.167543475 -1.204398270  0.409603607  0.313059401  2.241019033
 [637] -1.849477316 -0.511314187 -0.861028897 -0.275096146 -0.013592194  0.416918325
 [643] -0.819339718 -0.448176771 -0.913991960 -2.134927733  0.086011335 -1.370759283
 [649]  0.205967730  0.032884649 -1.370688658  0.282823139 -0.241088143  0.866359888
 [655] -1.651972309  1.007948166 -1.228866038 -0.270335234  1.934150713 -0.059523351
 [661]  0.517627020  1.103161344  0.114857818  1.039668867  1.386253598 -0.622998457
 [667] -1.147277685 -0.381073242  1.224137173  1.688732868 -1.615268897  0.607144517
 [673] -0.295636002  1.010071157  0.554794382 -0.784075043 -0.742817100 -0.722364142
 [679]  0.662257326 -0.582162529  0.455988387 -1.781119718 -0.629658204  0.409143357
 [685] -1.304239819  0.703055872  0.053664687 -0.979705281 -0.166381547 -1.018576099
 [691] -0.846122391 -1.211795043 -0.085538518 -1.534653546 -1.242610398 -1.878799269
 [697] -1.202799470  0.246259850  0.099437492 -0.124192683  1.135970661  0.409127988
 [703] -0.489991687 -1.246190951  0.558141217  1.292182537  0.452354334  0.953940773
 [709] -0.004243216  0.259701652 -0.987052395 -0.752721469  0.812494831 -2.139994429
 [715] -0.016973857 -0.464561836  0.314494470 -0.472386893 -0.156679199 -0.177207267
 [721] -2.746664998  0.323789522 -1.016719387 -1.482843672  0.612096612 -1.317720180
 [727] -1.733954785  1.506400794  0.314903508 -0.121437532  0.448216654 -0.708042128
 [733] -2.273746413 -0.550618610 -1.221762004 -1.369178485 -0.506519161 -1.292647628
 [739]  0.352386193 -0.160034433 -0.499896319 -1.223924783 -0.650632953 -0.600696251
 [745]  0.394231768 -0.391133922 -0.362805850  0.881253827 -2.097192328 -0.559451456
 [751] -0.560200310  1.342548126 -0.495997711 -0.222099810  0.538978143  0.728591548
 [757] -0.763764016  1.497111235 -0.207259874 -0.777997770 -1.978819043  1.526007686
 [763]  0.421892992  1.675802803 -1.028600937  0.843073303  0.639575897  0.069095212
 [769] -2.047699010  0.949766944  0.899087226  1.230343531  0.222748850  1.751299377
 [775] -1.285174780 -0.298256405 -1.324925765  0.280431669  0.499155929 -0.119885738
 [781] -0.912641463 -0.444878920  0.654418214  0.008747176  0.544494200  0.529594648
 [787] -0.737716131  0.931922967  0.043546971 -0.783964909  1.245723352 -0.320430779
 [793] -0.715308497  1.145406351 -0.538616516  1.220736794  0.126497085  3.170121063
 [799]  0.820672709 -2.472947428 -0.802167140  0.616764326  0.776380624 -0.957355119
 [805] -0.863721420 -1.043919684 -1.607002735  1.418231624 -1.062308011 -0.565649218
 [811]  0.237085626 -1.026781595  0.742212928  0.765532063 -1.021922125  0.422064792
 [817]  0.911331693  0.173452157  0.404634055 -1.340501446  1.186501824  0.314417914
 [823]  0.116577522  0.406708410  0.189341892 -0.131919411  2.624119310 -0.535998476
 [829] -1.053743317 -1.965298178  0.825177294 -1.159725009 -2.124142078 -1.204040849
 [835]  0.100834886 -0.208874834 -0.945512653  0.987488405 -0.507202473  0.654841582
 [841]  0.295101305  0.823253842 -0.776925790 -0.564030938 -2.285482209  2.920914916
 [847] -1.072649367 -0.607315292  0.499798642  0.610276216 -0.410700935 -0.498393278
 [853]  0.594026068 -1.453102362  0.144150943 -1.431190523 -0.743236141  1.094508117
 [859] -0.819109557  1.207075757 -0.688276011  0.500425235  1.027973047 -0.951643695
 [865] -0.749977072  0.822596233 -0.042732973  1.506599722  0.069695373  0.206297989
 [871] -0.640311316  0.400356436  0.389601603  0.372001762  1.166630654  0.152324487
 [877]  0.314969385  0.029796260 -1.019307528 -0.448106453  0.338213493 -0.427267178
 [883] -0.712700140 -0.610432866  1.539842537 -0.396948918 -2.203781316  1.136636410
 [889] -1.228091634 -0.228228385 -2.052530032  0.477791240  0.365338332  0.229380643
 [895]  2.838265954  1.147149237  0.162714377  0.781737434  0.180779637 -0.673162695
 [901]  0.351033845  1.143100225 -0.216864089  0.628046499 -0.638892189  1.696403349
 [907] -0.537651219  0.610194169 -0.317964325 -1.834098163 -0.502897050  0.328304037
 [913] -0.626066326  1.652803286  0.876607734 -1.206163528 -0.044169023  1.386871447
 [919]  0.096151908  2.566316004  0.446070794 -0.549925855  1.497654192  0.327764900
 [925] -1.423579153 -0.348794319 -1.192842200 -0.600116293 -0.255818156 -0.605363432
 [931]  0.060674248 -1.108285327  0.724070808  0.330012976 -0.394156025 -0.453875028
 [937]  0.232203747 -1.404808606  1.176402561 -0.562317965  0.054730132 -0.747585346
 [943]  2.015122696 -0.612356873  1.060752270 -0.170146326  2.006794826 -0.472229770
 [949] -1.098054909  0.516868033 -1.001880970  2.073570017  0.182279411 -0.581139945
 [955]  0.774140267 -0.409950905  0.668896777  0.379242398 -0.701289531  0.380350247
 [961] -0.548751079  0.914561856  2.757792850 -1.588085347 -0.725625816  0.908866390
 [967]  0.816872090 -0.654948049 -0.984589185 -0.123825679  0.372960800  0.114870838
 [973] -0.392016076 -0.850097321  0.214677459  1.445144076 -0.254692382 -0.831998246
 [979]  1.515698205 -1.541885256 -1.239427008 -0.449815622 -0.982189609  0.782643091
 [985] -0.789642937  1.766895845  1.247969561 -0.920154720  2.278112619 -1.601998917
 [991] -0.240590898  0.324374986  0.798755647 -2.006202992 -1.665270031 -1.141665862
 [997]  1.061770480  1.297549964 -0.614223911  1.120671373
 [ reached getOption("max.print") -- omitted 11897 entries ]

$rawpval
   [1] 0.0154340414 0.6715348028 0.7404894447 0.0246536951 0.8329002925 0.9142109559
   [7] 0.7341410393 0.5938067192 0.8493995689 0.0279645350 0.1858786901 0.6565753606
  [13] 0.9426987751 0.7092441494 0.4048381422 0.3406777190 0.9719836420 0.9876790500
  [19] 0.9919192378 0.3825073491 0.5766521069 0.6927360309 0.8667580837 0.8765872598
  [25] 0.0808219167 0.0694321442 0.1294621464 0.7536557054 0.1197316751 0.2112728125
  [31] 0.6108174748 0.9076288417 0.2618069652 0.0923718453 0.3693268402 0.8515895034
  [37] 0.4752654515 0.7986479259 0.5853502205 0.2741375473 0.0860905824 0.2604384703
  [43] 0.3245793856 0.0181541486 0.3200500863 0.3301287643 0.1039783471 0.7518056949
  [49] 0.9018506521 0.5711406209 0.4454791499 0.6149171354 0.5304810292 0.9539805691
  [55] 0.5626679728 0.5384363578 0.7003264667 0.9602366219 0.0389078353 0.1024875539
  [61] 0.8078010804 0.4444762079 0.1397491625 0.1499831569 0.7925817286 0.2427757595
  [67] 0.9527639693 0.7360443149 0.0268712649 0.0629831216 0.0771407708 0.6796945716
  [73] 0.3074510731 0.3699146603 0.0703419316 0.1250950972 0.3219126713 0.6574242386
  [79] 0.8644039295 0.6334161794 0.6468846360 0.5289584303 0.6550108120 0.4506031868
  [85] 0.8809377057 0.4464390961 0.8444492025 0.3498759012 0.5728158742 0.2423737522
  [91] 0.2712274018 0.0465628985 0.0628418630 0.0194350594 0.4199211414 0.5841599470
  [97] 0.6079173768 0.7656597904 0.8728829441 0.4306040246 0.0884426545 0.2109172546
 [103] 0.1245961917 0.8585804614 0.0632673877 0.0207932988 0.2389871301 0.3233359335
 [109] 0.9637096665 0.7950632460 0.0692242795 0.1508540781 0.8505951851 0.7475155596
 [115] 0.7573753271 0.3041989921 0.3721551344 0.1061968265 0.4690392946 0.4434340876
 [121] 0.6761170678 0.6920326690 0.0881015824 0.5605272714 0.8575435318 0.5328782051
 [127] 0.0927509273 0.5362294688 0.6251773895 0.9869804883 0.9563776430 0.7257354096
 [133] 0.3938452527 0.4216052248 0.5809880075 0.1640857936 0.2633837512 0.7691392824
 [139] 0.8674642582 0.7261652815 0.2552032171 0.9299371959 0.2560424354 0.1634332533
 [145] 0.8105403159 0.5638292361 0.6104614376 0.2216775830 0.1138188365 0.0283591848
 [151] 0.9521995313 0.7577835935 0.5562090350 0.4993259450 0.4195906281 0.0099457843
 [157] 0.0712296245 0.2569998547 0.7023844409 0.1551663395 0.3068255360 0.0760409499
 [163] 0.2474718727 0.3857119476 0.0967197861 0.0513187033 0.4322410546 0.6911768774
 [169] 0.1421086440 0.4326516948 0.2442334666 0.1825994731 0.8455082671 0.4572135714
 [175] 0.6122044951 0.9852399364 0.1518633679 0.4491527784 0.9644144625 0.9243391613
 [181] 0.9553416340 0.8363614713 0.8575910040 0.2462708272 0.4057749286 0.5133724661
 [187] 0.4437719903 0.4882394933 0.5077585399 0.8341346898 0.1385214831 0.5506402656
 [193] 0.2273364020 0.2346664747 0.2065047854 0.9113341144 0.6299337651 0.0877443141
 [199] 0.6104008227 0.2448352443 0.8595699186 0.3925630364 0.6673821525 0.1813340942
 [205] 0.0736335149 0.2175329468 0.4217133742 0.0939367720 0.4306200597 0.7569749362
 [211] 0.9309994809 0.1743629136 0.2957474795 0.4994127100 0.4926819496 0.6554388425
 [217] 0.6641453282 0.3664571360 0.4343632661 0.9042002543 0.4659127828 0.2943414673
 [223] 0.7506874203 0.6417389894 0.8652984851 0.1456051649 0.9804354375 0.4537383767
 [229] 0.7710981173 0.2506847329 0.9427761124 0.3129156988 0.9692522625 0.2259584391
 [235] 0.5640026348 0.6275409235 0.3239056645 0.5467044747 0.8460672268 0.9164721415
 [241] 0.4006603027 0.0067978487 0.1375075948 0.9207024550 0.4928608401 0.3759452775
 [247] 0.9720218833 0.8027411876 0.0600520014 0.0757059422 0.3917487362 0.9864629824
 [253] 0.2215832567 0.1236865467 0.2234536311 0.3379215267 0.9555140487 0.6826416475
 [259] 0.3470961568 0.4159846827 0.6397894219 0.7552896864 0.9003051648 0.4849518100
 [265] 0.7845946312 0.8243463797 0.9393965290 0.2212648634 0.4589246258 0.0347990523
 [271] 0.8743095736 0.8045393861 0.8992383342 0.6265696188 0.7638224585 0.9908315695
 [277] 0.3686294991 0.7619778214 0.7622252919 0.6789355558 0.8372679351 0.5248113494
 [283] 0.0854651268 0.7105310514 0.3019915207 0.2620996903 0.6362608101 0.5951354736
 [289] 0.4838700805 0.0801899705 0.5055746562 0.3104888528 0.1115761565 0.2354268253
 [295] 0.4933329116 0.7448860759 0.9834442451 0.6017357763 0.1329324834 0.9615912681
 [301] 0.7417679899 0.7197024066 0.5874322450 0.8124725125 0.9232060786 0.4994459431
 [307] 0.2968353555 0.5495432806 0.6624277390 0.7656338786 0.4723039769 0.3128435422
 [313] 0.0449670937 0.0182819741 0.4665665353 0.0507833528 0.1839648707 0.2879543166
 [319] 0.7796719185 0.5944838924 0.8387504486 0.8570831700 0.1302482866 0.9839598419
 [325] 0.4268726113 0.8396402671 0.8174321188 0.7063414068 0.5882863564 0.8948799559
 [331] 0.1843653730 0.3023377231 0.1977205246 0.8921762072 0.0763076286 0.0173837060
 [337] 0.2677627838 0.1438204811 0.6349003761 0.8605340913 0.2189940121 0.4300168449
 [343] 0.6839334725 0.2166129828 0.2011400504 0.0009095656 0.1224318408 0.3710299728
 [349] 0.5644600314 0.0348340683 0.6096435553 0.4083404762 0.5482683813 0.9191261202
 [355] 0.8626987044 0.1197285579 0.9375500401 0.3315511177 0.2582219818 0.8436348327
 [361] 0.1323244261 0.9821661120 0.1236089714 0.1667681548 0.2608089337 0.6763066403
 [367] 0.0898154329 0.1776886045 0.8912839162 0.2921985836 0.0963476567 0.1106172419
 [373] 0.3005685228 0.1519585707 0.4081698679 0.4921353388 0.6245659467 0.2728824507
 [379] 0.2313763688 0.5353955282 0.4873088074 0.9837907418 0.8295914251 0.9504164329
 [385] 0.0120473498 0.3116426871 0.6659166318 0.4640439429 0.9726258695 0.6734723199
 [391] 0.8973247687 0.5297840343 0.1196264789 0.7669513781 0.8482860104 0.3198675684
 [397] 0.4139315099 0.0812829683 0.6573022632 0.4941570382 0.5131935000 0.3870153873
 [403] 0.7116371810 0.1882030150 0.4991474779 0.9652088404 0.7241582014 0.4229397715
 [409] 0.6557741628 0.2539722810 0.6644371509 0.4058483499 0.9057880958 0.8379286395
 [415] 0.0536699441 0.8657802888 0.1167542386 0.7994582684 0.5330214524 0.5381419469
 [421] 0.2360982326 0.9940851481 0.8761228538 0.5490912561 0.6451179647 0.0225387284
 [427] 0.4327229057 0.1661441228 0.1608877283 0.6416954982 0.6594866856 0.1590533517
 [433] 0.1593352570 0.5064503781 0.2800862334 0.9172934285 0.9454891141 0.1018191330
 [439] 0.2356443265 0.1432252019 0.4184596912 0.9074363947 0.6012067195 0.0986183373
 [445] 0.2326702493 0.8008077802 0.3058865283 0.1713570148 0.7664730376 0.2463561326
 [451] 0.9637107088 0.6713611228 0.8125641802 0.3447523856 0.0705843500 0.1190691039
 [457] 0.9501548498 0.0530067160 0.7430787334 0.8452788690 0.2181623056 0.6141378746
 [463] 0.5927030653 0.5485723386 0.2940326946 0.5628690348 0.2010429489 0.0504619033
 [469] 0.0811410454 0.5818100617 0.3844958295 0.2185557774 0.4640743403 0.2295680815
 [475] 0.9699889645 0.5460640261 0.2647686575 0.2274075549 0.3214210314 0.5240610602
 [481] 0.8415429531 0.7916805148 0.9903870749 0.5637411461 0.2576199048 0.0394649438
 [487] 0.3299306802 0.7356689476 0.9826157810 0.0743744328 0.4322227228 0.2871174792
 [493] 0.4174304520 0.3650642398 0.6868058852 0.1436350523 0.1169494252 0.3239220267
 [499] 0.9805918325 0.8917318642 0.7838981958 0.6127683372 0.9810868409 0.4474028229
 [505] 0.7127051735 0.7116664429 0.5278949664 0.8569019117 0.5490006634 0.8851870306
 [511] 0.1706615487 0.6055746445 0.6401218443 0.1821855005 0.9126173965 0.9193022949
 [517] 0.3576225921 0.8161469304 0.7633730610 0.4852485451 0.7188329897 0.5686841669
 [523] 0.5648192000 0.5482010159 0.6453407576 0.3433839727 0.0661168330 0.4678540934
 [529] 0.4890925638 0.7477638575 0.6163894602 0.9773535985 0.6027871655 0.1176900432
 [535] 0.9191547551 0.9333651583 0.1066481395 0.5223066609 0.2635242035 0.6856724670
 [541] 0.2470111528 0.7973582777 0.5726429889 0.5191086395 0.1005119908 0.2280841282
 [547] 0.6494129699 0.6015487791 0.2166227770 0.2756739536 0.2516136706 0.0707327646
 [553] 0.6848299653 0.6371785820 0.7944219924 0.3971748418 0.4489979085 0.1637912485
 [559] 0.2222947067 0.7780403133 0.2146834417 0.1237051519 0.1961422910 0.3029692509
 [565] 0.9051215240 0.3566568929 0.7312115166 0.0794957232 0.2192950388 0.0279846450
 [571] 0.7669419594 0.8246085919 0.0735795936 0.4095183940 0.1127009949 0.2983933077
 [577] 0.3402087006 0.0650399178 0.9241168757 0.4241873501 0.1253949367 0.0385238565
 [583] 0.0001328583 0.2083686715 0.7309846175 0.2029558224 0.7620078138 0.3535088281
 [589] 0.5334287909 0.8359007225 0.8671301722 0.9471266519 0.0341960312 0.5498258098
 [595] 0.4499470309 0.5230829432 0.9701808647 0.6890483093 0.0269163866 0.5441493208
 [601] 0.7591157440 0.1404703526 0.3564438479 0.7757697601 0.7843744988 0.1250866520
 [607] 0.0409818149 0.0531286150 0.0553031620 0.6585727496 0.1929317346 0.4642194005
 [613] 0.2013341473 0.6184934745 0.3417799502 0.5294631076 0.8281247435 0.6843557012
 [619] 0.5483475208 0.7028379868 0.3409426774 0.4906954825 0.8855412208 0.9937018285
 [625] 0.0291808308 0.6706587916 0.3358709031 0.1134334150 0.0613492919 0.8837080132
 [631] 0.9416698512 0.4334712213 0.8857821598 0.3410483752 0.3771177666 0.0125124211
 [637] 0.9678055399 0.6954344639 0.8053889359 0.6083788138 0.5054223339 0.3383690747
 [643] 0.7937036910 0.6729871873 0.8196394649 0.9836165432 0.4657287033 0.9147749961
 [649] 0.4184080591 0.4868832871 0.9147639840 0.3886562081 0.5952565988 0.1931464169
 [655] 0.9507299021 0.1567396733 0.8904389816 0.6065488192 0.0265473001 0.5237323664
 [661] 0.3023592629 0.1349785524 0.4542789099 0.1492468848 0.0828347403 0.7333572321
 [667] 0.8743665632 0.6484255492 0.1104502424 0.0456353169 0.9468737619 0.2718775045
 [673] 0.6162459617 0.1562305999 0.2895176552 0.7835019607 0.7712037895 0.7649646865
 [679] 0.2539031603 0.7197713949 0.3241991645 0.9625535525 0.7355408832 0.3412172300
 [685] 0.9039240863 0.2410104655 0.4786011588 0.8363841908 0.5660716526 0.8457978720
 [691] 0.8012577646 0.8872045768 0.5340833627 0.9375655238 0.8929942818 0.9698640466
 [697] 0.8854730351 0.4027405441 0.4603954578 0.5494186423 0.1279844216 0.3412228687
 [703] 0.6879301094 0.8936528499 0.2883739788 0.0981469690 0.3255068696 0.1700568117
 [709] 0.5016927933 0.3975469591 0.8381915231 0.7741913471 0.2082538757 0.9838223915
 [715] 0.5067712642 0.6788773660 0.3765727580 0.6816746710 0.5622511595 0.5703272063
 [721] 0.9969897703 0.3730486945 0.8453565299 0.9309420251 0.2702369195 0.9062013317
 [727] 0.9585369470 0.0659821586 0.3764174591 0.5483277549 0.3269984225 0.7605404520
 [733] 0.9885093811 0.7090524257 0.8891011795 0.9145282552 0.6937538790 0.9019335206
 [739] 0.3622743275 0.5635730249 0.6914259579 0.8895096982 0.7423582735 0.7259788417
 [745] 0.3467049667 0.6521508762 0.6416250393 0.1890902257 0.9820117233 0.7120731740
 [751] 0.7123285922 0.0897091685 0.6900519869 0.5878819073 0.2949509680 0.2331257752
 [757] 0.7774960580 0.0671821584 0.5820965452 0.7817148362 0.9760818081 0.0635039752
 [763] 0.3365515626 0.0468884124 0.8481663812 0.1995937243 0.2612241782 0.4724569160
 [769] 0.9797052495 0.1711153429 0.1843031008 0.1092842448 0.4118654899 0.0399471775
 [775] 0.9006343927 0.6172462626 0.9074021128 0.3895731713 0.3088347697 0.5477131687
 [781] 0.8192844301 0.6717963715 0.2564212016 0.4965104263 0.2930507193 0.2981965031
 [787] 0.7696565161 0.1756881686 0.4826327612 0.7834696494 0.1064329888 0.6256791018
 [793] 0.7627907804 0.1260204343 0.7049242553 0.1110928459 0.4496692280 0.0007618772
 [799] 0.2059163598 0.9932998073 0.7887718591 0.2686950926 0.2187621407 0.8308059783
 [805] 0.8061295280 0.8517387248 0.9459731159 0.0780615759 0.8559520605 0.7141838688
 [811] 0.4062951818 0.8477383410 0.2289791686 0.2219773908 0.8465911200 0.3364888629
 [817] 0.1810603160 0.4311480156 0.3428732613 0.9099588127 0.1177120908 0.3766018261
 [823] 0.4535974257 0.3421110816 0.4249124320 0.5524759823 0.0043436674 0.7040201983
 [829] 0.8539997743 0.9753101318 0.2046354712 0.8769196078 0.9831708675 0.8857131048
 [835] 0.4598407666 0.5827270250 0.8278013930 0.1617016323 0.6939936199 0.2562848776
 [841] 0.3839582464 0.2051818281 0.7813987226 0.7136334613 0.9888577158 0.0017450256
 [847] 0.8582857670 0.7281791541 0.3086084336 0.2708394248 0.6593540795 0.6908965632
 [853] 0.2762473390 0.9269023323 0.4426906389 0.9238121906 0.7713306369 0.1368660947
 [859] 0.7936380451 0.1137014852 0.7543605075 0.3083878443 0.1519812532 0.8293611433
 [865] 0.7733657432 0.2053688208 0.5170428024 0.0659566443 0.4722180622 0.4182790747
 [871] 0.7390148874 0.3444470030 0.3484155833 0.3549457687 0.1216797793 0.4394655062
 [877] 0.3763924494 0.4881147706 0.8459715043 0.6729618143 0.3676011541 0.6654076320
 [883] 0.7619843349 0.7292124488 0.0617993703 0.6542974352 0.9862301364 0.1278451562
 [889] 0.8902937143 0.5902656522 0.9799409103 0.3163993985 0.3574294342 0.4092865398
 [895] 0.0022679685 0.1256599737 0.4353716634 0.2171844485 0.4282702737 0.7495781043
 [901] 0.3627814786 0.1264984904 0.5858428679 0.2649867397 0.7385534653 0.0449047599
 [907] 0.7045910688 0.2708665959 0.6247440008 0.9666802879 0.6924816725 0.3713408962
 [913] 0.7343642809 0.0491854526 0.1903498686 0.8861227782 0.5176151631 0.0827404830
 [919] 0.4616999630 0.0051392570 0.3277730545 0.7088148851 0.0671115595 0.3715447140
 [925] 0.9227158329 0.6363781357 0.8835344103 0.7257856327 0.6009543732 0.7275312287
 [931] 0.4758093203 0.8661306949 0.2345111326 0.3706950788 0.6532670751 0.6750406110
 [937] 0.4081898837 0.9199609003 0.1197170244 0.7130502999 0.4781767317 0.7726448468
 [943] 0.0219458974 0.7298491655 0.1444012493 0.5675524691 0.0223857570 0.6816186037
 [949] 0.8639097434 0.3026241422 0.8417994576 0.0190596311 0.4276817279 0.7194269306
 [955] 0.2194239238 0.6590790188 0.2517806633 0.3522539349 0.7584388270 0.3518427205
 [961] 0.7084118565 0.1802108455 0.0029096531 0.9438664794 0.7659659048 0.1817103284
 [967] 0.2070007646 0.7437493987 0.8375870479 0.5492733505 0.3545888098 0.4542737500
 [973] 0.6524768323 0.8023645096 0.4150093989 0.0742087104 0.6005196496 0.7972950326
 [979] 0.0647978409 0.9384492603 0.8924062976 0.6735783037 0.8369967771 0.2169183647
 [985] 0.7851318373 0.0386228365 0.1060211022 0.8212540437 0.0113599343 0.9454220755
 [991] 0.5950638975 0.3728270774 0.2122160564 0.9775827026 0.9520705527 0.8732035321
 [997] 0.1441699470 0.0972210128 0.7304663127 0.1312138860
 [ reached getOption("max.print") -- omitted 11897 entries ]

$adjpval
   [1] 0.7497685 0.9991656 0.9991656 0.8283865 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656
  [10] 0.8331399 0.9563232 0.9991656 0.9991656 0.9991656 0.9810876 0.9769232 0.9991656 0.9991656
  [19] 0.9991656 0.9779565 0.9991656 0.9991656 0.9991656 0.9991656 0.9559345 0.9427232 0.9563090
  [28] 0.9991656 0.9563090 0.9563232 0.9991656 0.9991656 0.9615904 0.9563090 0.9779565 0.9991656
  [37] 0.9938293 0.9991656 0.9991656 0.9637331 0.9563090 0.9615904 0.9766330 0.7740882 0.9750715
  [46] 0.9768710 0.9563090 0.9991656 0.9991656 0.9991656 0.9863318 0.9991656 0.9991656 0.9991656
  [55] 0.9991656 0.9991656 0.9991656 0.9991656 0.8845738 0.9563090 0.9991656 0.9863318 0.9563090
  [64] 0.9563090 0.9991656 0.9615904 0.9991656 0.9991656 0.8331399 0.9367125 0.9520426 0.9991656
  [73] 0.9740760 0.9779565 0.9442438 0.9563090 0.9752091 0.9991656 0.9991656 0.9991656 0.9991656
  [82] 0.9991656 0.9991656 0.9869955 0.9991656 0.9863318 0.9991656 0.9769232 0.9991656 0.9615904
  [91] 0.9633764 0.9010093 0.9367125 0.7857491 0.9841331 0.9991656 0.9991656 0.9991656 0.9991656
 [100] 0.9841331 0.9563090 0.9563232 0.9563090 0.9991656 0.9367125 0.8028497 0.9604914 0.9763607
 [109] 0.9991656 0.9991656 0.9427232 0.9563090 0.9991656 0.9991656 0.9991656 0.9737263 0.9779565
 [118] 0.9563090 0.9921600 0.9863318 0.9991656 0.9991656 0.9563090 0.9991656 0.9991656 0.9991656
 [127] 0.9563090 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9789923 0.9841331 0.9991656
 [136] 0.9563232 0.9615904 0.9991656 0.9991656 0.9991656 0.9615904 0.9991656 0.9615904 0.9563232
 [145] 0.9991656 0.9991656 0.9991656 0.9563232 0.9563090 0.8331399 0.9991656 0.9991656 0.9991656
 [154] 0.9958961 0.9841331 0.7052595 0.9470603 0.9615904 0.9991656 0.9563090 0.9740760 0.9520426
 [163] 0.9615904 0.9779565 0.9563090 0.9119009 0.9846913 0.9991656 0.9563090 0.9846913 0.9615904
 [172] 0.9563232 0.9991656 0.9917186 0.9991656 0.9991656 0.9563090 0.9863318 0.9991656 0.9991656
 [181] 0.9991656 0.9991656 0.9991656 0.9615904 0.9810876 0.9991656 0.9863318 0.9946664 0.9989493
 [190] 0.9991656 0.9563090 0.9991656 0.9563232 0.9571148 0.9563232 0.9991656 0.9991656 0.9563090
 [199] 0.9991656 0.9615904 0.9991656 0.9785244 0.9991656 0.9563232 0.9505945 0.9563232 0.9841331
 [208] 0.9563090 0.9841331 0.9991656 0.9991656 0.9563232 0.9693473 0.9958961 0.9946664 0.9991656
 [217] 0.9991656 0.9779565 0.9846913 0.9991656 0.9919322 0.9693473 0.9991656 0.9991656 0.9991656
 [226] 0.9563090 0.9991656 0.9903372 0.9991656 0.9615904 0.9991656 0.9750715 0.9991656 0.9563232
 [235] 0.9991656 0.9991656 0.9764516 0.9991656 0.9991656 0.9991656 0.9796253 0.6662638 0.9563090
 [244] 0.9991656 0.9946664 0.9779565 0.9991656 0.9991656 0.9351472 0.9520426 0.9783275 0.9991656
 [253] 0.9563232 0.9563090 0.9563232 0.9769232 0.9991656 0.9991656 0.9769232 0.9830037 0.9991656
 [262] 0.9991656 0.9991656 0.9945522 0.9991656 0.9991656 0.9991656 0.9563232 0.9917186 0.8659243
 [271] 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9779565 0.9991656 0.9991656
 [280] 0.9991656 0.9991656 0.9991656 0.9563090 0.9991656 0.9726302 0.9615904 0.9991656 0.9991656
 [289] 0.9945522 0.9549951 0.9987073 0.9747350 0.9563090 0.9571148 0.9946664 0.9991656 0.9991656
 [298] 0.9991656 0.9563090 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9958961
 [307] 0.9702028 0.9991656 0.9991656 0.9991656 0.9931992 0.9750715 0.9006636 0.7740882 0.9919322
 [316] 0.9119009 0.9563232 0.9685067 0.9991656 0.9991656 0.9991656 0.9991656 0.9563090 0.9991656
 [325] 0.9841331 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9563232 0.9729360 0.9563232
 [334] 0.9991656 0.9520426 0.7740882 0.9625628 0.9563090 0.9991656 0.9991656 0.9563232 0.9841331
 [343] 0.9991656 0.9563232 0.9563232 0.5667503 0.9563090 0.9779565 0.9991656 0.8659243 0.9991656
 [352] 0.9817809 0.9991656 0.9991656 0.9991656 0.9563090 0.9991656 0.9768710 0.9615904 0.9991656
 [361] 0.9563090 0.9991656 0.9563090 0.9563232 0.9615904 0.9991656 0.9563090 0.9563232 0.9991656
 [370] 0.9693473 0.9563090 0.9563090 0.9712935 0.9563090 0.9817809 0.9946664 0.9991656 0.9637331
 [379] 0.9571148 0.9991656 0.9946664 0.9991656 0.9991656 0.9991656 0.7250023 0.9750715 0.9991656
 [388] 0.9919322 0.9991656 0.9991656 0.9991656 0.9991656 0.9563090 0.9991656 0.9991656 0.9750715
 [397] 0.9819908 0.9559345 0.9991656 0.9947056 0.9991656 0.9779565 0.9991656 0.9563232 0.9958961
 [406] 0.9991656 0.9991656 0.9841331 0.9991656 0.9615904 0.9991656 0.9810876 0.9991656 0.9991656
 [415] 0.9174157 0.9991656 0.9563090 0.9991656 0.9991656 0.9991656 0.9572332 0.9993746 0.9991656
 [424] 0.9991656 0.9991656 0.8094867 0.9846913 0.9563232 0.9563232 0.9991656 0.9991656 0.9563232
 [433] 0.9563232 0.9987073 0.9645079 0.9991656 0.9991656 0.9563090 0.9571148 0.9563090 0.9841128
 [442] 0.9991656 0.9991656 0.9563090 0.9571148 0.9991656 0.9738528 0.9563232 0.9991656 0.9615904
 [451] 0.9991656 0.9991656 0.9991656 0.9769232 0.9442986 0.9563090 0.9991656 0.9148194 0.9991656
 [460] 0.9991656 0.9563232 0.9991656 0.9991656 0.9991656 0.9693473 0.9991656 0.9563232 0.9119009
 [469] 0.9559345 0.9991656 0.9779565 0.9563232 0.9919322 0.9564470 0.9991656 0.9991656 0.9615904
 [478] 0.9563232 0.9750715 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9615904 0.8845738
 [487] 0.9768710 0.9991656 0.9991656 0.9505945 0.9846913 0.9674888 0.9836493 0.9779565 0.9991656
 [496] 0.9563090 0.9563090 0.9764516 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9863318
 [505] 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9563232 0.9991656 0.9991656
 [514] 0.9563232 0.9991656 0.9991656 0.9772691 0.9991656 0.9991656 0.9945522 0.9991656 0.9991656
 [523] 0.9991656 0.9991656 0.9991656 0.9769232 0.9367125 0.9919322 0.9946664 0.9991656 0.9991656
 [532] 0.9991656 0.9991656 0.9563090 0.9991656 0.9991656 0.9563090 0.9991656 0.9615904 0.9991656
 [541] 0.9615904 0.9991656 0.9991656 0.9991656 0.9563090 0.9563232 0.9991656 0.9991656 0.9563232
 [550] 0.9637331 0.9615904 0.9443483 0.9991656 0.9991656 0.9991656 0.9791901 0.9863318 0.9563232
 [559] 0.9563232 0.9991656 0.9563232 0.9563090 0.9563232 0.9735525 0.9991656 0.9772691 0.9991656
 [568] 0.9547697 0.9563232 0.8331399 0.9991656 0.9991656 0.9505945 0.9817809 0.9563090 0.9708110
 [577] 0.9769232 0.9367125 0.9991656 0.9841331 0.9563090 0.8845738 0.3758299 0.9563232 0.9991656
 [586] 0.9563232 0.9991656 0.9772691 0.9991656 0.9991656 0.9991656 0.9991656 0.8659243 0.9991656
 [595] 0.9866888 0.9991656 0.9991656 0.9991656 0.8331399 0.9991656 0.9991656 0.9563090 0.9772691
 [604] 0.9991656 0.9991656 0.9563090 0.8845738 0.9148194 0.9259042 0.9991656 0.9563232 0.9919322
 [613] 0.9563232 0.9991656 0.9769232 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9769232
 [622] 0.9946664 0.9991656 0.9993746 0.8349900 0.9991656 0.9768710 0.9563090 0.9358057 0.9991656
 [631] 0.9991656 0.9846913 0.9991656 0.9769232 0.9779565 0.7250023 0.9991656 0.9991656 0.9991656
 [640] 0.9991656 0.9987073 0.9769232 0.9991656 0.9991656 0.9991656 0.9991656 0.9919322 0.9991656
 [649] 0.9841128 0.9946664 0.9991656 0.9779565 0.9991656 0.9563232 0.9991656 0.9563090 0.9991656
 [658] 0.9991656 0.8313444 0.9991656 0.9729360 0.9563090 0.9903372 0.9563090 0.9563090 0.9991656
 [667] 0.9991656 0.9991656 0.9563090 0.9010093 0.9991656 0.9637331 0.9991656 0.9563090 0.9685067
 [676] 0.9991656 0.9991656 0.9991656 0.9615904 0.9991656 0.9764516 0.9991656 0.9991656 0.9769232
 [685] 0.9991656 0.9610509 0.9938293 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656
 [694] 0.9991656 0.9991656 0.9991656 0.9991656 0.9807675 0.9917186 0.9991656 0.9563090 0.9769232
 [703] 0.9991656 0.9991656 0.9685067 0.9563090 0.9768710 0.9563232 0.9969256 0.9791901 0.9991656
 [712] 0.9991656 0.9563232 0.9991656 0.9988828 0.9991656 0.9779565 0.9991656 0.9991656 0.9991656
 [721] 0.9994118 0.9779565 0.9991656 0.9991656 0.9625628 0.9991656 0.9991656 0.9367125 0.9779565
 [730] 0.9991656 0.9768710 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656
 [739] 0.9778945 0.9991656 0.9991656 0.9991656 0.9991656 0.9991656 0.9769232 0.9991656 0.9991656
 [748] 0.9563232 0.9991656 0.9991656 0.9991656 0.9563090 0.9991656 0.9991656 0.9693473 0.9571148
 [757] 0.9991656 0.9387305 0.9991656 0.9991656 0.9991656 0.9367125 0.9768710 0.9010093 0.9991656
 [766] 0.9563232 0.9615904 0.9931992 0.9991656 0.9563232 0.9563232 0.9563090 0.9819908 0.8845738
 [775] 0.9991656 0.9991656 0.9991656 0.9779565 0.9740760 0.9991656 0.9991656 0.9991656 0.9615904
 [784] 0.9956879 0.9693473 0.9708110 0.9991656 0.9563232 0.9944903 0.9991656 0.9563090 0.9991656
 [793] 0.9991656 0.9563090 0.9991656 0.9563090 0.9864576 0.5667503 0.9563232 0.9993746 0.9991656
 [802] 0.9625628 0.9563232 0.9991656 0.9991656 0.9991656 0.9991656 0.9547697 0.9991656 0.9991656
 [811] 0.9810876 0.9991656 0.9563232 0.9563232 0.9991656 0.9768710 0.9563232 0.9845718 0.9769232
 [820] 0.9991656 0.9563090 0.9779565 0.9903372 0.9769232 0.9841331 0.9991656 0.6498137 0.9991656
 [829] 0.9991656 0.9991656 0.9563232 0.9991656 0.9991656 0.9991656 0.9917186 0.9991656 0.9991656
 [838] 0.9563232 0.9991656 0.9615904 0.9779565 0.9563232 0.9991656 0.9991656 0.9991656 0.6313345
 [847] 0.9991656 0.9991656 0.9740760 0.9633764 0.9991656 0.9991656 0.9637331 0.9991656 0.9863318
 [856] 0.9991656 0.9991656 0.9563090 0.9991656 0.9563090 0.9991656 0.9740760 0.9563090 0.9991656
 [865] 0.9991656 0.9563232 0.9991656 0.9367125 0.9931992 0.9841128 0.9991656 0.9769232 0.9769232
 [874] 0.9772691 0.9563090 0.9849272 0.9779565 0.9946664 0.9991656 0.9991656 0.9779565 0.9991656
 [883] 0.9991656 0.9991656 0.9367125 0.9991656 0.9991656 0.9563090 0.9991656 0.9991656 0.9991656
 [892] 0.9750715 0.9772691 0.9817809 0.6313345 0.9563090 0.9846913 0.9563232 0.9841331 0.9991656
 [901] 0.9778945 0.9563090 0.9991656 0.9615904 0.9991656 0.9006636 0.9991656 0.9633764 0.9991656
 [910] 0.9991656 0.9991656 0.9779565 0.9991656 0.9010093 0.9563232 0.9991656 0.9991656 0.9563090
 [919] 0.9919322 0.6498137 0.9768710 0.9991656 0.9387305 0.9779565 0.9991656 0.9991656 0.9991656
 [928] 0.9991656 0.9991656 0.9991656 0.9938293 0.9991656 0.9571148 0.9779565 0.9991656 0.9991656
 [937] 0.9817809 0.9991656 0.9563090 0.9991656 0.9938293 0.9991656 0.8074293 0.9991656 0.9563090
 [946] 0.9991656 0.8094867 0.9991656 0.9991656 0.9731022 0.9991656 0.7740882 0.9841331 0.9991656
 [955] 0.9563232 0.9991656 0.9615904 0.9772691 0.9991656 0.9772691 0.9991656 0.9563232 0.6359581
 [964] 0.9991656 0.9991656 0.9563232 0.9563232 0.9991656 0.9991656 0.9991656 0.9772691 0.9903372
 [973] 0.9991656 0.9991656 0.9830037 0.9505945 0.9991656 0.9991656 0.9367125 0.9991656 0.9991656
 [982] 0.9991656 0.9991656 0.9563232 0.9991656 0.8845738 0.9563090 0.9991656 0.7250023 0.9991656
 [991] 0.9991656 0.9779565 0.9563232 0.9991656 0.9991656 0.9991656 0.9563090 0.9563090 0.9991656
[1000] 0.9563090
 [ reached getOption("max.print") -- omitted 11897 entries ]

hist(invnormcomb$rawpval, breaks=100, col="grey", main="Inverse normal method", xlab = "Raw p-values Ecotype in Heat (meta-analysis)")

## Summarize the results. DE are the results from the original anlayses of the datasets individually.
EH_results_metastats <- data.frame(DE, "DE.fishercomb_adjP"=fishcomb$adjpval, "DE.invnorm_adj_P"=invnormcomb$adjpval)
library(data.table)
setDT(EH_results_metastats, keep.rownames = "Gene")  # make the rownames a part of the dataframe with column name "Gene"
head(EH_results_metastats)

EH_results <- data.frame(DE, "DE.fishercomb"=ifelse(fishcomb$adjpval<=0.05,1,0), "DE.invnorm"=ifelse(invnormcomb$adjpval<=0.05,1,0))
head(EH_results)

## Dealing with conflicts in the sign of differential expression 
signsFC <- mapply(EH_FC, FUN=function(x) sign(x))
sumsigns <- apply(signsFC,1,sum)
commonsngFC <- ifelse(abs(sumsigns)==dim(signsFC)[2], sign(sumsigns), 0)
unionDE <- unique(c(fishcomb$DEindices, invnormcomb$DEindicies))
FC.selecDE <- data.frame(EH_results[unionDE,], do.call(cbind,EH_FC) [unionDE,], signFC=commonsngFC[unionDE], DE=DE[unionDE])
  # keep the ones that don't have a conflict (1 or -1)
keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),]
conflictDE <- FC.selecDE[which(FC.selecDE$signFC==0),]
head(FC.selecDE)  # the ones with conflict = 0

head(keepDE)

head(conflictDE)

table(conflictDE$DE)

< table of extent 0 >

### Merge results and write to  a file
setDT(FC.selecDE, keep.rownames = "Gene")
head(FC.selecDE)

head(top.table_EH_intersect)

head(EH_results_metastats)

#Heat_results <- merge(top.table_H_intersect,FC.selecDE, H_results_metastats, by.x = "Gene", all.x = TRUE)
EcotypeHeat_results <- merge(top.table_EH_intersect,FC.selecDE, all.x = TRUE)
EcotypeHeat_results2 <- merge(EcotypeHeat_results, EH_results_metastats, all.x = TRUE)
head(EcotypeHeat_results2)

write.table(EcotypeHeat_results2, file = "OutputFiles/Meta_EcotypeHeat.txt", row.names = F, sep = "\t", quote = F)

Make summary tables

Ecotype in Heat

## Make a table of results
fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)]
invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)]
indstudy_de <- list(rownames(keepDE)[which(keepDE[,"DE.2008Dataset"]==1)],rownames(keepDE)[which(keepDE[,"DE.2012Dataset"]==1)])
IDD.IRR(fishcomb_de,indstudy_de)

  DE  IDD Loss  IDR  IRR 
   0    0    0  NaN  NaN 

IDD.IRR(invnorm_de, indstudy_de)

  DE  IDD Loss  IDR  IRR 
   0    0    0  NaN  NaN 

Make Upset Plot, Ecotype in Heat

# Ecotype in Heat Treatment 2008
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_EH08_top <- top.table_H_Ecotypes08[which(top.table_H_Ecotypes08$adj.P.Val<=0.05), ]
dim(UP_EC08_top)

[1] 0 7

  # converting column of "Gene" in dataframe column into vector by passing as index
EH_HS2008 = UP_EH08_top[['Gene']]
# Ecotype in Heat Treatment 2012
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_EH12_top <- top.table_H_Ecotypes12[which(top.table_H_Ecotypes12$adj.P.Val<=0.05), ]
dim(UP_EH12_top)

[1] 0 7

  #converting column of "Gene" in dataframe column into vector by passing as index
EH_HS2012 = UP_EH12_top[['Gene']]
# Ecotypes in Heat MetaAnalysis
## Keep the genes from the meta-analysis that have invernorm adjusted p-value <= 0.05 and the log fold change is going in the same direction in both analyses
UP_MetaA <- EcotypeHeat_results2[which(EcotypeHeat_results2$DE.invnorm_adj_P<=0.05), ]
UP_MetaB <- UP_MetaA[which(abs(UP_MetaA$signFC)==1),]
dim(UP_MetaB)

[1]  0 23

  #converting column of "Gene" in dataframe column into vector by passing as index
Ecotypes_Heat_Meta = UP_MetaB[['Gene']]

listEcoHeat <- list(HS2008 = EH_HS2008, HS2012 = EH_HS2012, MetaAnalysis = Ecotypes_Heat_Meta)

### No significant genes for the Interaction so don't need to make upset plot
# Make upset put and save as a .pdf file
#pdf("OutputGraphs/Upset_EcotypesInHeat.pdf")
  #make plot
#upset(fromList(listEcoHeat), mainbar.y.label = "Number of Genes in Intersection", sets.x.label = "Differentially expressed genes (adjusted p-value=0.05)",order.by = "freq")
#dev.off()

##  Heat vs Control in Lake (fast-living) ecotype Meta-analysis
first get the data ready

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI01lcmdlIHRvcCB0YWJsZXMgcmVzdWx0cyBmb3IgZWFjaCBkYXRhc2V0IC0ga2VlcCB0aGUgZ2VuZXMgaW4gY29tbW9uIH4xMzAwMFxubGlicmFyeShwbHlyKVxudG9wLnRhYmxlX0hDX0xha2UgPC0gbWVyZ2UodG9wLnRhYmxlX0hDX0xha2UwOCx0b3AudGFibGVfSENfTGFrZTEyLGJ5PVwiR2VuZVwiLGFsbC54ID0gRkFMU0UsIHN1ZmZpeGVzID0gYyhcIi4wOFwiLCBcIi4xMlwiKSlcbmRpbSh0b3AudGFibGVfSENfTGFrZSlcbmBgYCJ9 -->

```r
#Merge top tables results for each dataset - keep the genes in common ~13000
library(plyr)
top.table_HC_Lake <- merge(top.table_HC_Lake08,top.table_HC_Lake12,by="Gene",all.x = FALSE, suffixes = c(".08", ".12"))
dim(top.table_HC_Lake)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEyODk3ICAgIDEzXG4ifQ== -->

```
[1] 12897    13
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3VtbWFyeSh0b3AudGFibGVfSENfTGFrZSlcbmBgYCJ9 -->

```r
summary(top.table_HC_Lake)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin 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 -->

```
     Gene              logFC.08          AveExpr.08          t.08            P.Value.08    
 Length:12897       Min.   :-7.37738   Min.   :-2.530   Min.   :-6.32164   Min.   :0.0000  
 Class :character   1st Qu.:-0.35657   1st Qu.: 1.378   1st Qu.:-0.72043   1st Qu.:0.2202  
 Mode  :character   Median :-0.03835   Median : 3.343   Median :-0.08059   Median :0.4928  
                    Mean   : 0.07903   Mean   : 3.261   Mean   : 0.05756   Mean   :0.4878  
                    3rd Qu.: 0.37757   3rd Qu.: 4.970   3rd Qu.: 0.68682   3rd Qu.:0.7524  
                    Max.   :10.58086   Max.   :15.915   Max.   :12.35001   Max.   :0.9999  
  adj.P.Val.08            B.08           logFC.12           AveExpr.12          t.12         
 Min.   :0.0001357   Min.   :-6.660   Min.   :-4.629616   Min.   :-5.122   Min.   :-8.29038  
 1st Qu.:0.8642004   1st Qu.:-6.311   1st Qu.:-0.213915   1st Qu.: 1.965   1st Qu.:-0.81747  
 Median :0.9782121   Median :-5.829   Median :-0.004383   Median : 3.609   Median :-0.01693  
 Mean   :0.8925686   Mean   :-5.575   Mean   : 0.004801   Mean   : 3.543   Mean   : 0.01604  
 3rd Qu.:0.9921070   3rd Qu.:-5.261   3rd Qu.: 0.206506   3rd Qu.: 5.057   3rd Qu.: 0.82491  
 Max.   :0.9999112   Max.   : 9.954   Max.   :10.596908   Max.   :16.730   Max.   : 8.78417  
   P.Value.12      adj.P.Val.12            B.12       
 Min.   :0.0000   Min.   :0.0003335   Min.   :-6.477  
 1st Qu.:0.1651   1st Qu.:0.6738808   1st Qu.:-6.183  
 Median :0.4213   Median :0.8469639   Median :-5.797  
 Mean   :0.4429   Mean   :0.7725906   Mean   :-5.417  
 3rd Qu.:0.7074   3rd Qu.:0.9442211   3rd Qu.:-5.144  
 Max.   :0.9997   Max.   :0.9997341   Max.   : 8.880  
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZCh0b3AudGFibGVfSENfTGFrZSlcbmBgYCJ9 -->

```r
head(top.table_HC_Lake)
```

<!-- rnb-source-end -->

<!-- rnb-frame-begin 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 -->

<div data-pagedtable="false">
  <script data-pagedtable-source type="application/json">
{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["Gene"],"name":[1],"type":["chr"],"align":["left"]},{"label":["logFC.08"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["AveExpr.08"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["t.08"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["P.Value.08"],"name":[5],"type":["dbl"],"align":["right"]},{"label":["adj.P.Val.08"],"name":[6],"type":["dbl"],"align":["right"]},{"label":["B.08"],"name":[7],"type":["dbl"],"align":["right"]},{"label":["logFC.12"],"name":[8],"type":["dbl"],"align":["right"]},{"label":["AveExpr.12"],"name":[9],"type":["dbl"],"align":["right"]},{"label":["t.12"],"name":[10],"type":["dbl"],"align":["right"]},{"label":["P.Value.12"],"name":[11],"type":["dbl"],"align":["right"]},{"label":["adj.P.Val.12"],"name":[12],"type":["dbl"],"align":["right"]},{"label":["B.12"],"name":[13],"type":["dbl"],"align":["right"]}],"data":[{"1":"A1CF","2":"-0.188163820","3":"6.907570","4":"-0.66070213","5":"0.5199828","6":"0.9795016","7":"-6.426092","8":"0.47196181","9":"7.083357","10":"2.84466297","11":"0.01030535","12":"0.2439468","13":"-2.981586","_rn_":"1"},{"1":"AAAS","2":"-0.176947955","3":"1.997824","4":"-0.33800589","5":"0.7405872","6":"0.9921070","7":"-5.910299","8":"-0.10297058","9":"1.900927","10":"-0.35696275","11":"0.72502167","12":"0.9478437","13":"-5.978054","_rn_":"2"},{"1":"AACS","2":"1.787795176","3":"1.485685","4":"2.16591315","5":"0.0488669","6":"0.6187439","7":"-3.933005","8":"0.58380371","9":"1.825707","10":"1.74055196","11":"0.09780141","12":"0.5730297","13":"-4.645358","_rn_":"3"},{"1":"AADAT","2":"-0.243315511","3":"4.079697","4":"-0.44762150","5":"0.6615656","6":"0.9921070","7":"-6.381815","8":"0.22031136","9":"4.845994","10":"1.50800507","11":"0.14786864","12":"0.6505769","13":"-5.364434","_rn_":"4"},{"1":"AAGAB","2":"0.009665561","3":"5.983880","4":"0.02726245","5":"0.9786516","6":"0.9989688","7":"-6.632558","8":"-0.01680265","9":"6.234108","10":"-0.09394985","11":"0.92612469","12":"0.9880790","13":"-6.458935","_rn_":"5"},{"1":"AAK1","2":"0.090025947","3":"5.952346","4":"0.39651704","5":"0.6979560","6":"0.9921070","7":"-6.553394","8":"0.17882922","9":"5.027270","10":"0.97901974","11":"0.33977313","12":"0.8103108","13":"-5.988441","_rn_":"6"}],"options":{"columns":{"min":{},"max":[10],"total":[13]},"rows":{"min":[10],"max":[10],"total":[6]},"pages":{}}}
  </script>
</div>

<!-- rnb-frame-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuXG4jUHV0IHAtdmFsdWUgYW5kIEZvbGQgY2hhbmdlcyByZXN1bHRzIGluIGxpc3RzXG5IQ19MYWtlX3Jhd3B2YWwgPC0gbGlzdChcInB2YWx1ZV8wOFwiPXRvcC50YWJsZV9IQ19MYWtlW1tcIlAuVmFsdWUuMDhcIl1dLCBcInB2YWx1ZV8xMlwiPXRvcC50YWJsZV9IQ19MYWtlW1tcIlAuVmFsdWUuMTJcIl1dKVxuc3VtbWFyeShIQ19MYWtlX3Jhd3B2YWwpXG5gYGAifQ== -->

```r

#Put p-value and Fold changes results in lists
HC_Lake_rawpval <- list("pvalue_08"=top.table_HC_Lake[["P.Value.08"]], "pvalue_12"=top.table_HC_Lake[["P.Value.12"]])
summary(HC_Lake_rawpval)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgIExlbmd0aCBDbGFzcyAgTW9kZSAgIFxucHZhbHVlXzA4IDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucHZhbHVlXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->

```
          Length Class  Mode   
pvalue_08 12897  -none- numeric
pvalue_12 12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuSENfTGFrZV9hZGpwdmFsIDwtIGxpc3QoXCJhZGpwdmFsdWVfMDhcIj10b3AudGFibGVfSENfTGFrZVtbXCJhZGouUC5WYWwuMDhcIl1dLCBcImFkanB2YWx1ZV8xMlwiPXRvcC50YWJsZV9IQ19MYWtlW1tcImFkai5QLlZhbC4xMlwiXV0pXG5zdW1tYXJ5KEhDX0xha2VfYWRqcHZhbClcbmBgYCJ9 -->

```r
HC_Lake_adjpval <- list("adjpvalue_08"=top.table_HC_Lake[["adj.P.Val.08"]], "adjpvalue_12"=top.table_HC_Lake[["adj.P.Val.12"]])
summary(HC_Lake_adjpval)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgIExlbmd0aCBDbGFzcyAgTW9kZSAgIFxuYWRqcHZhbHVlXzA4IDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuYWRqcHZhbHVlXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->

```
             Length Class  Mode   
adjpvalue_08 12897  -none- numeric
adjpvalue_12 12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuSENfTGFrZV9GQyA8LSBsaXN0KFwiSENfTGFrZV9GQ18wOFwiPXRvcC50YWJsZV9IQ19MYWtlW1tcImxvZ0ZDLjA4XCJdXSwgXCJIQ19MYWtlX0ZDXzEyXCI9dG9wLnRhYmxlX0hDX0xha2VbW1wibG9nRkMuMTJcIl1dKVxuc3VtbWFyeShIQ19MYWtlX0ZDKVxuYGBgIn0= -->

```r
HC_Lake_FC <- list("HC_Lake_FC_08"=top.table_HC_Lake[["logFC.08"]], "HC_Lake_FC_12"=top.table_HC_Lake[["logFC.12"]])
summary(HC_Lake_FC)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkhDX0xha2VfRkNfMDggMTI4OTcgIC1ub25lLSBudW1lcmljXG5IQ19MYWtlX0ZDXzEyIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xuIn0= -->

```
              Length Class  Mode   
HC_Lake_FC_08 12897  -none- numeric
HC_Lake_FC_12 12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI21ha2UgbWF0cml4IG9mIDEgZm9yIGdlbmVzIERFIGFuZCAwIGZvciBub3QuXG5ERTwtIG1hcHBseShIQ19MYWtlX2FkanB2YWwsIEZVTj1mdW5jdGlvbih4KSBpZmVsc2UoeCA8PSAwLjA1LCAxLCAwKSlcbnN0dWRpZXMgPC1jKFwiMjAwOERhdGFzZXRcIiwgXCIyMDEyRGF0YXNldFwiKVxuY29sbmFtZXMoREUpPXBhc3RlKFwiREVcIiwgc3R1ZGllcyxzZXA9XCIuXCIpXG5yb3duYW1lcyhERSk9cGFzdGUodG9wLnRhYmxlX0hDX0xha2UkXCJHZW5lXCIpXG4jREU8LWNiaW5kKHRvcC50YWJsZV9IQy1MYWtlZSRcIkdlbmVcIiwgREUpXG5zdW1tYXJ5KERFKVxuYGBgIn0= -->

```r
#make matrix of 1 for genes DE and 0 for not.
DE<- mapply(HC_Lake_adjpval, FUN=function(x) ifelse(x <= 0.05, 1, 0))
studies <-c("2008Dataset", "2012Dataset")
colnames(DE)=paste("DE", studies,sep=".")
rownames(DE)=paste(top.table_HC_Lake$"Gene")
#DE<-cbind(top.table_HC-Lakee$"Gene", DE)
summary(DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiIERFLjIwMDhEYXRhc2V0ICAgICBERS4yMDEyRGF0YXNldCAgICBcbiBNaW4uICAgOjAuMDAwMDAwICAgTWluLiAgIDowLjAwMDAwMCAgXG4gMXN0IFF1LjowLjAwMDAwMCAgIDFzdCBRdS46MC4wMDAwMDAgIFxuIE1lZGlhbiA6MC4wMDAwMDAgICBNZWRpYW4gOjAuMDAwMDAwICBcbiBNZWFuICAgOjAuMDAxNzA2ICAgTWVhbiAgIDowLjAwNjM1OCAgXG4gM3JkIFF1LjowLjAwMDAwMCAgIDNyZCBRdS46MC4wMDAwMDAgIFxuIE1heC4gICA6MS4wMDAwMDAgICBNYXguICAgOjEuMDAwMDAwICBcbiJ9 -->

```
 DE.2008Dataset     DE.2012Dataset    
 Min.   :0.000000   Min.   :0.000000  
 1st Qu.:0.000000   1st Qu.:0.000000  
 Median :0.000000   Median :0.000000  
 Mean   :0.001706   Mean   :0.006358  
 3rd Qu.:0.000000   3rd Qu.:0.000000  
 Max.   :1.000000   Max.   :1.000000  
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChERSlcbmBgYCJ9 -->

```r
head(DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgREUuMjAwOERhdGFzZXQgREUuMjAxMkRhdGFzZXRcbkExQ0YgICAgICAgICAgICAgICAwICAgICAgICAgICAgICAwXG5BQUFTICAgICAgICAgICAgICAgMCAgICAgICAgICAgICAgMFxuQUFDUyAgICAgICAgICAgICAgIDAgICAgICAgICAgICAgIDBcbkFBREFUICAgICAgICAgICAgICAwICAgICAgICAgICAgICAwXG5BQUdBQiAgICAgICAgICAgICAgMCAgICAgICAgICAgICAgMFxuQUFLMSAgICAgICAgICAgICAgIDAgICAgICAgICAgICAgIDBcbiJ9 -->

```
      DE.2008Dataset DE.2012Dataset
A1CF               0              0
AAAS               0              0
AACS               0              0
AADAT              0              0
AAGAB              0              0
AAK1               0              0
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZGltKERFKVxuYGBgIn0= -->

```r
dim(DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEyODk3ICAgICAyXG4ifQ== -->

```
[1] 12897     2
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI0NoZWNrIGZvciBub3JtYWwgZGlzdHJpYnV0aW9uc1xucGFyKG1mcm93ID0gYygxLDIpKVxuaGlzdChIQ19MYWtlX3Jhd3B2YWxbWzFdXSwgYnJlYWtzPTEwMCwgY29sPVwiZ3JleVwiLCBtYWluPVwiMjAwOCBEYXRhc2V0XCIsIHhsYWI9XCJSYXcgcC12YWx1ZXNcIilcbmhpc3QoSENfTGFrZV9yYXdwdmFsW1syXV0sIGJyZWFrcz0xMDAsIGNvbD1cImdyZXlcIiwgbWFpbj1cIjIwMTIgRGF0YXNldFwiLCB4bGFiPVwiUmF3IHAtdmFsdWVzXCIpXG5gYGAifQ== -->

```r
#Check for normal distributions
par(mfrow = c(1,2))
hist(HC_Lake_rawpval[[1]], breaks=100, col="grey", main="2008 Dataset", xlab="Raw p-values")
hist(HC_Lake_rawpval[[2]], breaks=100, col="grey", main="2012 Dataset", xlab="Raw p-values")
```

<!-- rnb-source-end -->

<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

<img 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" />

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### Calculate the meta-analysis p-value
Heat vs Control in Lake (fast-living) ecotype

<!-- rnb-text-end -->


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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubGlicmFyeShtZXRhUk5BU2VxKVxuIyBQLXZhbHVlIGNvbWJpbmF0aW9uIHVzaW5nIEZpc2hlciBtZXRob2QgXG5maXNoY29tYiA8LSBmaXNoZXJjb21iKEhDX0xha2VfcmF3cHZhbCwgQkh0aCA9IDAuMDUpXG5zdW1tYXJ5KGZpc2hjb21iKVxuYGBgIn0= -->

```r
library(metaRNASeq)
# P-value combination using Fisher method 
fishcomb <- fishercomb(HC_Lake_rawpval, BHth = 0.05)
summary(fishcomb)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkRFaW5kaWNlcyAgICAgICAxNTIgIC1ub25lLSBudW1lcmljXG5UZXN0U3RhdGlzdGljIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucmF3cHZhbCAgICAgICAxMjg5NyAgLW5vbmUtIG51bWVyaWNcbmFkanB2YWwgICAgICAgMTI4OTcgIC1ub25lLSBudW1lcmljXG4ifQ== -->

```
              Length Class  Mode   
DEindices       152  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric
```



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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGlzdChmaXNoY29tYiRyYXdwdmFsLCBicmVha3M9MTAwLCBjb2w9XCJncmV5XCIsIG1haW49XCJGaXNoZXIgTWV0aG9kXCIsIHhsYWIgPSBcIlJhdyBwLXZhbHVlcyAobWV0YS1hbmFseXNpcylcIilcbmBgYCJ9 -->

```r
hist(fishcomb$rawpval, breaks=100, col="grey", main="Fisher Method", xlab = "Raw p-values (meta-analysis)")
```

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<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

<img 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" />

<!-- rnb-plot-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBQLXZhbHVlIGNvbWJpbmF0aW9uIHVzaW5nIGludmVyc2Ugbm9tcmFsIG1ldGhvZCBtZXRob2QgXG5pbnZub3JtY29tYiA8LSBpbnZub3JtKEhDX0xha2VfcmF3cHZhbCwgbnJlcD1jKDEyLDIwKSwgQkh0aD0wLjA1KVxuc3VtbWFyeShpbnZub3JtY29tYilcbmBgYCJ9 -->

```r
# P-value combination using inverse nomral method method 
invnormcomb <- invnorm(HC_Lake_rawpval, nrep=c(12,20), BHth=0.05)
summary(invnormcomb)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgICAgICAgICBMZW5ndGggQ2xhc3MgIE1vZGUgICBcbkRFaW5kaWNlcyAgICAgICAxNTUgIC1ub25lLSBudW1lcmljXG5UZXN0U3RhdGlzdGljIDEyODk3ICAtbm9uZS0gbnVtZXJpY1xucmF3cHZhbCAgICAgICAxMjg5NyAgLW5vbmUtIG51bWVyaWNcbmFkanB2YWwgICAgICAgMTI4OTcgIC1ub25lLSBudW1lcmljXG4ifQ== -->

```
              Length Class  Mode   
DEindices       155  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGVhZChpbnZub3JtY29tYilcbmBgYCJ9 -->

```r
head(invnormcomb)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin {"data":"$DEindices\n  [1]   127   241   267   292   354   367   406   719   792   794   823   980  1286  1428  1443\n [16]  1566  1643  1889  1895  1919  2013  2096  2215  2224  2226  2227  2231  2390  2449  2710\n [31]  2770  2848  2913  3000  3066  3115  3144  3328  3365  3391  3513  3514  3670  3697  3704\n [46]  3705  3706  3708  3712  3716  3718  3822  3998  4006  4033  4078  4087  4097  4269  4270\n [61]  4600  4673  4716  4776  4882  5004  5240  5253  5296  5310  5333  5402  5414  5465  5566\n [76]  5578  5638  5752  5758  5791  5800  5901  6184  6185  6215  6234  6331  6339  6695  6751\n [91]  6801  6892  6908  6919  7305  7508  7571  7627  7671  7673  8452  8517  8636  8701  8723\n[106]  8781  8789  8814  8875  8909  9043  9191  9256  9365  9393  9425  9537  9578  9903  9922\n[121]  9943  9949  9951 10001 10092 10295 10324 10383 10458 10504 10562 10564 10666 10763 10843\n[136] 11107 11147 11228 11256 11295 11330 11331 11398 11481 11563 11873 11937 11999 12359 12439\n[151] 12611 12653 12701 12749 12816\n\n$TestStatistic\n   [1]  1.799514205 -0.867698432  2.037192675  0.571433901 -2.385440124  0.009036393\n   [7]  2.394161699 -0.908478865  0.379173135  0.209522574 -0.035353240 -1.410954957\n  [13] -1.389507873  0.444954783 -0.394559880 -0.274516127  0.554755539  0.653170841\n  [19]  1.853807683 -0.297206620 -1.575411103 -0.785531599 -1.389572176 -1.036129130\n  [25]  2.173925370 -0.013212432  1.008171535  1.334242858  0.031221936  0.236373836\n  [31]  2.131568295 -0.884401602 -0.392739672 -1.529063888 -0.919470618 -0.100327943\n  [37]  1.616258596 -1.091203220 -1.334020440  1.991074386  0.749191998  0.535593081\n  [43]  1.063919862  0.564043744  2.011198390  3.130503416  1.542436171 -0.941545524\n  [49]  1.733612134  0.494569652  2.304121579  1.258087871  0.430328890  1.818190982\n  [55] -1.144177620  1.368231538  0.454360268 -0.435967962  0.199435322  0.432595537\n  [61] -0.799033252  0.521501056  0.014425041  0.623104260  0.913412282  1.899311376\n  [67]  0.922076200  0.695025385  0.543879642  0.458066378  2.000623047  1.734424620\n  [73]  1.105587334 -0.452160768  1.594878247 -0.044774594 -1.610628794 -0.745308432\n  [79] -0.422101661 -0.907460769 -1.155513073  0.204471690  0.695761078  0.242127642\n  [85] -0.739807162 -0.513343546  1.097144750 -0.004088184  0.296698460  0.328443062\n  [91] -1.182464517 -1.424267397 -0.875115261 -0.024368032 -0.502765924  1.730528898\n  [97]  0.915285063 -0.780855525 -0.364058652 -0.036677596  3.218096185 -0.098423977\n [103]  1.284968570 -0.643678740 -1.651362843  0.907798015  1.917244091 -0.832221925\n [109]  0.057963428 -0.071907285  1.972498991  1.589676329  1.062886981 -1.352287370\n [115]  0.517867691  0.409898352  1.002923374  1.140791800  0.755688353 -1.105228719\n [121]  1.105302986  1.846006508  0.556539907 -0.699376712 -0.452884570 -2.244520205\n [127]  4.251251310 -1.610077664  0.090096485  0.812327093 -1.383150041 -0.692496725\n [133] -0.529678495 -1.035676810 -1.710087720  0.032481960  0.763165137  0.578608429\n [139]  2.997697166  2.824714898  1.486745190  1.008100648 -1.349417970  0.602809350\n [145]  2.679550670 -0.432224706  1.568269458 -0.087133123  0.375446716 -0.150781069\n [151]  1.568883797  2.101368464  1.239940167  0.077900214 -0.934680653  2.197023318\n [157]  0.315326608  2.509405375  1.603456389  0.503831968  0.708466342  1.988689455\n [163]  1.417879561  1.171091751 -1.361057209  0.576153360  0.354060533  0.166822902\n [169] -0.069320378  1.092582157  1.198190596 -1.929647803 -1.679197474  0.491508082\n [175] -1.348336010 -1.109755655  1.264696171 -1.750452896 -0.871494042 -0.768263266\n [181]  0.094460673 -0.747997174  0.544764296 -0.584005091 -0.541802660 -1.269089813\n [187] -0.276299966  1.836964384  0.866330879  0.500755962 -0.580244695  0.500527187\n [193] -0.549190288 -1.257913461  0.795578563  0.301849879 -0.307894072 -0.484856605\n [199] -0.908331662  0.442093686 -0.318830896  0.209433613 -0.242237114  1.579762409\n [205] -0.278324693  0.894505078 -0.494750481  1.519406102  2.340974907  1.380278962\n [211]  0.054987144 -0.676397220  0.521345279 -1.230469430 -0.052040353  0.678764051\n [217] -0.152034143  1.118348500  1.360906987  0.520035246  0.760559905  0.824496619\n [223]  1.427296729  0.973746626 -0.440924074  0.458681065  2.065186022 -1.269279292\n [229]  2.295719118  0.184864002 -0.246091391 -2.041187327 -0.753192073 -1.819332552\n [235]  2.220440328  0.197162248 -0.237219101  0.029842367  1.344320456 -1.318854527\n [241]  3.672706415  1.254959595  0.147213404 -0.549777892  1.448317444 -0.946011338\n [247] -0.344413813  0.126332378  1.331531976 -0.667636259 -0.675175371  0.304666385\n [253]  0.161819043  0.940646314  2.243977112  0.480856397 -0.422680195  0.316180698\n [259]  0.224377104 -0.204715431 -0.530315932  0.049829169  0.304093614  0.080937054\n [265] -0.074827368 -0.431243908  6.660431903  0.197363960 -0.569711528  0.080041362\n [271] -1.438380006 -3.045227123 -1.090077404 -0.109979888 -1.532888844 -0.157680852\n [277] -1.796954253 -0.714009882  0.092628351 -0.579197728  0.081228670 -1.385179082\n [283] -0.862804087 -1.399451882 -0.594016448  1.578471914  0.102005227  0.937159808\n [289]  1.366447935  2.451103375  0.618628000  6.070051394  2.846182221 -0.659588277\n [295]  2.057454204  0.026150494 -0.174401341 -0.140826691 -0.765225619  0.051900382\n [301] -1.451250709 -1.802079624  0.019830480 -0.359481231 -1.411121715 -0.788083133\n [307]  0.580538908  0.520878532  1.091234743  1.727405543  1.248078324  1.327653406\n [313]  0.556942367  1.015582086 -0.540345247  1.935474628 -0.586445906  1.975837330\n [319]  0.354159183 -1.071999591 -0.913448506 -1.903139549 -2.123262012 -1.108840166\n [325] -0.085086984 -0.467252521  1.338380508 -0.801984469 -0.420760362 -0.329665064\n [331] -0.108240860 -2.035729816 -0.501758046 -0.849921994  0.458555714  0.636648449\n [337]  2.292646119  2.866314611  1.222006973  2.152750858  0.609784147 -0.188900268\n [343] -1.349033795  1.328117497  1.436523172 -0.277143803 -1.175003131 -0.901318638\n [349]  0.096659271  2.821024362  0.170534862 -0.394494970  0.209099272  3.303696534\n [355]  0.464128314  1.387292752  0.314918273  1.447470387  0.444961818 -1.308731798\n [361]  0.781823513  0.190023555  0.162705076 -1.634644211  0.164950712  1.091557033\n [367]  4.155249881  1.279378544 -1.295183476  0.577147118  1.505049314 -1.541265983\n [373] -1.587364277 -0.675295294 -0.381069227 -1.747042652  0.394586621  0.270981357\n [379]  0.861872906 -0.316994745 -0.705267407 -1.196515011  1.000984668  2.740578109\n [385]  0.552796490  0.604088306  2.366176419  1.646608611 -0.443878974 -1.079327997\n [391] -1.423610071  0.466625561 -0.209590767 -1.752062816 -0.457056457  0.509870786\n [397] -0.303544053 -0.318340827  0.046673883  0.346625158  1.006270621  0.331442818\n [403] -0.075668648  0.142673067  1.281893436  3.880062022  0.047458219  2.005394103\n [409] -0.973085073  0.225024328  0.405775595  0.765902118 -0.490809171 -1.082610157\n [415]  2.113041070 -0.212424979  0.537727564  2.627855197  2.102003192 -0.700842539\n [421]  0.374950362  2.139733389  1.568530111  0.898423309 -0.090022567  0.218983089\n [427] -2.940898765  0.222094591 -1.041126666 -0.547560253  1.868637988 -0.796007864\n [433]  0.912306922 -0.129660971 -0.692423839 -0.167855478 -0.298966712  0.527358933\n [439]  0.874411299 -0.171522746  0.074753084  0.005564998  3.199322325 -0.336327005\n [445]  1.281863807 -0.434680529  2.494663924  0.484561677  0.686276898  0.039425908\n [451] -0.578490261 -1.916314096 -0.070233547 -0.031673116  1.598027805  1.980029612\n [457] -1.214814278  0.184484608 -0.318471785 -0.687720678  0.958824585  0.069382527\n [463] -0.827541648  0.549306698  1.060660976 -0.089884863  1.557364850  0.670585636\n [469]  0.029623845  1.859530175 -0.045635421  2.240990705  0.429709186  0.969104656\n [475]  1.742601642  0.744097675 -0.641210569 -0.869357608  1.069117514 -0.559819296\n [481] -1.297885102 -2.242153954  0.847949371 -0.423575421  1.252185615 -0.040789205\n [487]  1.653008480  0.236936742  0.616715089  0.959273822 -0.187811397 -0.516387348\n [493] -0.010606410 -1.133204616 -0.092154901  2.417005676  0.692423115 -0.111185852\n [499]  0.513013870  1.858104367  1.931453413  1.396139960  0.010937806 -0.488826706\n [505]  1.907962581  0.125769823  0.460532951  1.171536988 -0.677939274  0.078562369\n [511] -1.292482134 -1.491995560 -0.036726247  0.014246223 -1.046855608  0.196269066\n [517]  1.193897362 -1.470890361 -0.091872078  1.436398733  0.512823351  0.491723868\n [523]  1.231834931 -0.648946976  0.888306131  2.370938894  0.796198529  2.821276723\n [529]  0.173660442 -0.832948882  0.538180183  0.380570532  1.691452833  2.359854493\n [535]  0.200914718  0.359097541  0.786912670 -0.831375660  0.419981496  0.491911967\n [541]  0.107071279  1.920155930  0.341651631  1.001619317  2.513821732  0.244462969\n [547]  2.412191005 -0.142569403  0.110211308 -0.042563466  0.449864235  1.795653128\n [553]  0.266470186  0.339919632 -1.142222353  1.450445978  0.824793474  1.322132567\n [559]  0.267180187 -0.828981241  0.133329677 -1.292959704  2.655278144  0.985658847\n [565] -1.416647880  2.651736224 -0.719499478  1.479192064  0.932620516 -1.249659486\n [571]  2.098598520  0.968459137  0.015691811  0.486366213 -0.413995812  0.530069861\n [577]  0.483237078  0.253934894  2.472597005 -0.050159016 -0.755686123 -0.319478344\n [583]  1.450541587 -0.987418496  0.074463752  2.656946856 -1.435407505 -0.709596731\n [589] -0.458681095 -0.320410180 -0.179561363 -0.825249435  0.597965606  0.189032225\n [595] -0.722699205  0.585830094  1.132344373  0.557600302  1.116002100 -1.718406050\n [601] -1.672900244 -0.376275023 -2.539311550  0.712178514 -1.176422663 -2.000294208\n [607] -1.265809687  1.254637299  0.131662442 -0.235110661 -0.043413377 -1.128328480\n [613]  0.959307486  0.021331615  0.145809085 -0.011701488 -0.144620316 -0.881819702\n [619] -0.484161249  1.179881300  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[691]  1.162012276  0.879319442  2.750373731 -0.542424787 -0.933513687  0.345345902\n [697]  0.932129936 -0.486070408 -0.312832584  0.506467244  1.772874384  1.369822131\n [703] -1.315119529  0.255097386  3.185425252  0.546800259  0.131408115  0.170114680\n [709]  1.742519108  0.263662188  1.138279531  0.363108672  3.179414872  0.401634561\n [715] -1.500624228 -1.785002826 -0.325190609 -1.907693353  3.340820011  1.590041128\n [721] -0.255033847  1.063320094  0.054243644 -0.689573196 -0.654445909  0.009465723\n [727]  0.178069270  2.003094056  0.499798595 -0.279716594 -1.155423600 -0.801586580\n [733] -0.467668237  0.691399703 -0.753217349  0.477667627 -1.696707747 -0.578707968\n [739] -0.385199994  0.799873980 -0.028511103  0.043678673  0.685578232 -0.171294665\n [745] -0.227078039  0.205666569  1.293321758 -1.559845669 -1.862379932  0.135072939\n [751]  0.305325413 -0.786683324  1.246966391 -0.411865559  0.608142680 -0.484234905\n [757] -1.325282761  2.063236004 -1.254649000  0.084509143  0.257512011 -0.040407945\n [763] -0.997995878  0.552984733 -0.776178482 -0.076914950  0.463845479  0.494937102\n [769] -0.315640573  0.453346233  0.525672725 -1.764311199 -0.506635777  0.433238989\n [775] -0.816522753  0.236786062  1.861838742 -0.233220568 -0.490697792 -0.759159648\n [781]  0.722533015  0.766809213  0.282087681 -0.327737497 -0.150122424 -2.141294534\n [787] -1.096243650 -1.981211388 -1.723688211 -0.498896007 -1.858089730  3.701346926\n [793] -0.925926164  3.390036843  0.358670667 -0.060304583  0.215789980  2.341465104\n [799] -0.487990029  1.864913906  0.106655159  0.115398198 -1.092275631  1.190108326\n [805]  0.681228008 -0.779358585  0.630124819  0.763450499  1.366049375 -1.151790677\n [811] -1.723957567  1.911910395 -0.176433439 -1.514012078 -1.443027270  0.227391983\n [817] -1.555802479 -0.252714697  0.530003358  0.393449844  0.977426767 -0.436464208\n [823]  4.688307377  1.096424769 -0.050772192 -1.251477843  0.669923390  2.323373973\n [829]  1.941444537  3.089820193  0.052516695  1.226151601 -1.576640899  0.162860792\n [835]  0.983080597 -0.278034064  0.455524154  1.908174004 -1.060413660  0.238722501\n [841] -0.266486386 -0.339807442 -1.580830096 -0.683710087  0.591487400  0.820413958\n [847]  0.610754977  3.034370904 -0.250004449 -0.880590381  2.410561273 -0.756579659\n [853]  1.642192426  1.015113726  1.359218427  0.313745434  0.440080545  1.235055662\n [859]  1.095096919 -1.221906140  1.240750138  0.809775840  0.289514400  1.803049628\n [865] -1.355127318 -0.050158974 -1.785225621 -0.693167144  2.099223825 -1.289286150\n [871]  0.997150226 -1.090374609  2.512639099  2.081428884 -1.031873230 -0.204590262\n [877] -0.639168918 -1.324014701 -0.271252490  0.258121467 -0.364486794 -0.065416976\n [883]  0.292017251  0.802685048  1.167160758 -1.341692663 -1.623845015 -1.993580543\n [889]  2.075740295  3.213846590  0.106048723 -0.612602324  2.517528550  1.213985669\n [895]  0.886578723  0.556619109 -0.354969149 -0.615802985 -1.484228060  0.591683708\n [901] -1.695927620  0.047758946 -1.631993542  0.932719168  0.787551443  0.353419078\n [907]  2.421532029  1.019672219  0.954130035  0.756794758 -1.112249260  1.101035278\n [913] -0.826982205 -1.146188407 -1.090356891  0.087970853  0.650004353  0.534580022\n [919]  0.223427823  1.841917102  0.703374521  1.208034659  0.785289481  0.202887892\n [925] -2.553222597  0.330408023  2.137819558 -0.170194883 -0.409905785  2.600966565\n [931] -0.848032597  0.983558154 -1.220673215 -0.464510380 -0.102964768 -1.057535693\n [937] -1.594494227  0.702176283 -0.053352298 -0.661315678 -1.475252836 -0.851655177\n [943] -1.780511397  0.111620975 -1.389334311  1.105964353  2.924168151 -0.381703904\n [949]  0.292969288  0.955746339  0.695986200  2.520728542  0.730008418  0.263395011\n [955]  1.721797237 -0.730804918 -0.112602594  0.392109395 -0.841449818 -0.432330779\n [961] -1.985958224  0.010151702  1.967127255  0.619945839  0.947891231  0.460403550\n [967]  0.498302675  0.317833278  1.871580388  1.267450550  1.623740263 -1.665593038\n [973]  0.767878074 -0.200285988  0.190632538 -0.857934986 -1.142393450 -0.242131411\n [979]  0.737116057  4.062900402 -2.728276329 -0.953881480 -0.646303520 -1.668594045\n [985] -0.149378771 -0.054256598  0.529154580  1.134702965  2.070549893  0.036796457\n [991]  0.310845148 -0.161562885 -1.660473952 -0.775021136  0.323099546 -1.515684428\n [997]  1.726218509 -0.446848689 -0.327986055  0.337597987\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n\n$rawpval\n   [1] 3.596869e-02 8.072203e-01 2.081537e-02 2.838528e-01 9.914707e-01 4.963950e-01\n   [7] 8.329201e-03 8.181874e-01 3.522796e-01 4.170202e-01 5.141010e-01 9.208711e-01\n  [13] 9.176608e-01 3.281762e-01 6.534161e-01 6.081560e-01 2.895309e-01 2.568231e-01\n  [19] 3.188334e-02 6.168456e-01 9.424192e-01 7.839290e-01 9.176706e-01 8.499290e-01\n  [25] 1.485537e-02 5.052708e-01 1.566861e-01 9.106214e-02 4.875463e-01 4.065713e-01\n  [31] 1.652118e-02 8.117603e-01 6.527441e-01 9.368757e-01 8.210753e-01 5.399580e-01\n  [37] 5.301921e-02 8.624083e-01 9.089014e-01 2.323635e-02 2.268707e-01 2.961199e-01\n  [43] 1.436825e-01 2.863622e-01 2.215225e-02 8.725348e-04 6.148382e-02 8.267873e-01\n  [49] 4.149346e-02 3.104520e-01 1.060791e-02 1.041800e-01 3.334782e-01 3.451747e-02\n  [55] 8.737250e-01 8.561981e-02 3.247848e-01 6.685700e-01 4.209611e-01 3.326543e-01\n  [61] 7.878644e-01 3.010089e-01 4.942454e-01 2.666080e-01 1.805129e-01 2.876177e-02\n  [67] 1.782444e-01 2.435197e-01 2.932621e-01 3.234524e-01 2.271651e-02 4.142138e-02\n  [73] 1.344526e-01 6.744234e-01 5.536973e-02 5.178565e-01 9.463697e-01 7.719574e-01\n  [79] 6.635246e-01 8.179184e-01 8.760598e-01 4.189925e-01 2.432892e-01 4.043406e-01\n  [85] 7.702915e-01 6.961445e-01 1.362891e-01 5.016309e-01 3.833484e-01 3.712883e-01\n  [91] 8.814893e-01 9.228155e-01 8.092444e-01 5.097205e-01 6.924356e-01 4.176791e-02\n  [97] 1.800210e-01 7.825563e-01 6.420929e-01 5.146290e-01 6.452227e-04 5.392022e-01\n [103] 9.940163e-02 7.401081e-01 9.506677e-01 1.819925e-01 2.760346e-02 7.973582e-01\n [109] 4.768889e-01 5.286622e-01 2.427634e-02 5.595389e-02 1.439166e-01 9.118583e-01\n [115] 3.022753e-01 3.409403e-01 1.579489e-01 1.269783e-01 2.249180e-01 8.654698e-01\n [121] 1.345142e-01 3.244563e-02 2.889209e-01 7.578417e-01 6.746841e-01 9.876005e-01\n [127] 1.062897e-05 9.463095e-01 4.641053e-01 2.083020e-01 9.166906e-01 7.556873e-01\n [133] 7.018326e-01 8.498235e-01 9.563752e-01 4.870439e-01 2.226825e-01 2.814267e-01\n [139] 1.360139e-03 2.366135e-03 6.854106e-02 1.567031e-01 9.113986e-01 2.733178e-01\n [145] 3.686052e-03 6.672109e-01 5.840913e-02 5.347172e-01 3.536641e-01 5.599258e-01\n [151] 5.833751e-02 1.780432e-02 1.074988e-01 4.689537e-01 8.250235e-01 1.400939e-02\n [157] 3.762568e-01 6.046731e-03 5.441697e-02 3.071897e-01 2.393279e-01 2.336775e-02\n [163] 7.811297e-02 1.207809e-01 9.132522e-01 2.822558e-01 3.616468e-01 4.337547e-01\n [169] 5.276327e-01 1.372887e-01 1.154214e-01 9.731748e-01 9.534432e-01 3.115336e-01\n [175] 9.112248e-01 8.664478e-01 1.029901e-01 9.599799e-01 8.082578e-01 7.788346e-01\n [181] 4.623716e-01 7.727691e-01 2.929578e-01 7.203916e-01 7.060228e-01 8.977955e-01\n [187] 6.088412e-01 3.310758e-02 1.931544e-01 3.082714e-01 7.191252e-01 3.083520e-01\n [193] 7.085626e-01 8.957885e-01 2.131385e-01 3.813833e-01 6.209185e-01 6.861110e-01\n [199] 8.181485e-01 3.292107e-01 6.250726e-01 4.170549e-01 5.957018e-01 5.708064e-02\n [205] 6.096184e-01 1.855259e-01 6.896119e-01 6.433015e-02 9.616730e-03 8.375038e-02\n [211] 4.780744e-01 7.506058e-01 3.010631e-01 8.907393e-01 5.207517e-01 2.486437e-01\n [217] 5.604200e-01 1.317091e-01 8.677154e-02 3.015195e-01 2.234600e-01 2.048287e-01\n [223] 7.674719e-02 1.650912e-01 6.703660e-01 3.232316e-01 1.945270e-02 8.978293e-01\n [229] 1.084597e-02 4.266679e-01 5.971943e-01 9.793839e-01 7.743327e-01 9.655696e-01\n [235] 1.319445e-02 4.218503e-01 5.937566e-01 4.880964e-01 8.942239e-02 9.063911e-01\n [241] 1.199976e-04 1.047467e-01 4.414818e-01 7.087641e-01 7.376415e-02 8.279286e-01\n [247] 6.347324e-01 4.497344e-01 9.150702e-02 7.478171e-01 7.502178e-01 3.803101e-01\n [253] 4.357242e-01 1.734431e-01 1.241694e-02 3.153093e-01 6.637357e-01 3.759327e-01\n [259] 4.112319e-01 5.811028e-01 7.020535e-01 4.801293e-01 3.805283e-01 4.677460e-01\n [265] 5.298240e-01 6.668545e-01 1.365119e-11 4.217714e-01 7.155633e-01 4.681022e-01\n [271] 9.248369e-01 9.988375e-01 8.621605e-01 5.437873e-01 9.373484e-01 5.626459e-01\n [277] 9.638286e-01 7.623895e-01 4.630994e-01 7.187721e-01 4.676300e-01 9.170011e-01\n [283] 8.058774e-01 9.191612e-01 7.237494e-01 5.722862e-02 4.593763e-01 1.743382e-01\n [289] 8.589921e-02 7.120952e-03 2.680807e-01 6.393466e-10 2.212343e-03 7.452410e-01\n [295] 1.982128e-02 4.895687e-01 5.692250e-01 5.559966e-01 7.779314e-01 4.793040e-01\n [301] 9.266450e-01 9.642336e-01 4.920893e-01 6.403824e-01 9.208956e-01 7.846760e-01\n [307] 2.807756e-01 3.012257e-01 1.375848e-01 4.204743e-02 1.060012e-01 9.214632e-02\n [313] 2.887834e-01 1.549142e-01 7.055205e-01 2.646604e-02 7.212120e-01 2.408660e-02\n [319] 3.616098e-01 8.581399e-01 8.194966e-01 9.714888e-01 9.831340e-01 8.662504e-01\n [325] 5.339039e-01 6.798404e-01 9.038621e-02 7.887190e-01 6.630350e-01 6.291735e-01\n [331] 5.430977e-01 9.791113e-01 6.920811e-01 8.023158e-01 3.232766e-01 2.621769e-01\n [337] 1.093419e-02 2.076407e-03 1.108525e-01 1.566913e-02 2.710024e-01 5.749145e-01\n [343] 9.113369e-01 9.206965e-02 7.542676e-02 6.091652e-01 8.800033e-01 8.162905e-01\n [349] 4.614985e-01 2.393528e-03 4.322948e-01 6.533922e-01 4.171854e-01 4.770953e-04\n [355] 3.212779e-01 8.267626e-02 3.764119e-01 7.388261e-02 3.281737e-01 9.046874e-01\n [361] 2.171592e-01 4.246453e-01 4.353753e-01 9.489382e-01 4.344914e-01 1.375139e-01\n [367] 1.624661e-05 1.003819e-01 9.023715e-01 2.819200e-01 6.615570e-02 9.383740e-01\n [373] 9.437849e-01 7.502559e-01 6.484241e-01 9.596850e-01 3.465740e-01 3.932027e-01\n [379] 1.943787e-01 6.243762e-01 7.596781e-01 8.842522e-01 1.584171e-01 3.066560e-03\n [385] 2.902014e-01 2.728925e-01 8.986439e-03 4.981926e-02 6.714350e-01 8.597792e-01\n [391] 9.227203e-01 3.203839e-01 5.830065e-01 9.601185e-01 6.761848e-01 3.050710e-01\n [397] 6.192624e-01 6.248868e-01 4.813866e-01 3.644365e-01 1.571427e-01 3.701550e-01\n [403] 5.301586e-01 4.432742e-01 9.994002e-02 5.221492e-05 4.810740e-01 2.246047e-02\n [409] 8.347445e-01 4.109802e-01 3.424537e-01 2.218673e-01 6.882193e-01 8.605093e-01\n [415] 1.729863e-02 5.841123e-01 2.953826e-01 4.296254e-03 1.777650e-02 7.582994e-01\n [421] 3.538487e-01 1.618816e-02 5.837873e-02 1.844800e-01 5.358654e-01 4.133316e-01\n [427] 9.983637e-01 4.121201e-01 8.510916e-01 7.080031e-01 3.083660e-02 7.869863e-01\n [433] 1.808036e-01 5.515827e-01 7.556644e-01 5.666515e-01 6.175173e-01 2.989722e-01\n [439] 1.909472e-01 5.680936e-01 4.702056e-01 4.977799e-01 6.887553e-04 6.316879e-01\n [445] 9.994521e-02 6.681028e-01 6.303824e-03 3.139937e-01 2.462693e-01 4.842754e-01\n [451] 7.185334e-01 9.723374e-01 5.279961e-01 5.126336e-01 5.501839e-02 2.385010e-02\n [457] 8.877815e-01 4.268167e-01 6.249365e-01 7.541857e-01 1.688236e-01 4.723426e-01\n [463] 7.960349e-01 2.913975e-01 1.444220e-01 5.358106e-01 5.969194e-02 2.512423e-01\n [469] 4.881835e-01 3.147601e-02 5.181996e-01 1.251334e-02 3.337036e-01 1.662465e-01\n [475] 4.070161e-02 2.284087e-01 7.393071e-01 8.076742e-01 1.425084e-01 7.121986e-01\n [481] 9.028366e-01 9.875243e-01 1.982331e-01 6.640623e-01 1.052511e-01 5.162680e-01\n [487] 4.916457e-02 4.063529e-01 2.687113e-01 1.687104e-01 5.744877e-01 6.972080e-01\n [493] 5.042313e-01 8.714358e-01 5.367125e-01 7.824387e-03 2.443358e-01 5.442655e-01\n [499] 3.039708e-01 3.157710e-02 2.671350e-02 8.133618e-02 4.956365e-01 6.875178e-01\n [505] 2.819803e-02 4.499571e-01 3.225669e-01 1.206915e-01 7.510949e-01 4.686904e-01\n [511] 9.019049e-01 9.321498e-01 5.146484e-01 4.943168e-01 8.524169e-01 4.221998e-01\n [517] 1.162591e-01 9.293396e-01 5.366002e-01 7.544446e-02 3.040374e-01 3.114573e-01\n [523] 1.090054e-01 7.418137e-01 1.871881e-01 8.871482e-03 2.129583e-01 2.391646e-03\n [529] 4.310662e-01 7.975632e-01 2.952263e-01 3.517610e-01 4.537517e-02 9.141052e-03\n [535] 4.203826e-01 3.597611e-01 2.156665e-01 7.971193e-01 3.372495e-01 3.113908e-01\n [541] 4.573662e-01 2.741910e-02 3.663065e-01 1.582637e-01 5.971539e-03 4.034361e-01\n [547] 7.928486e-03 5.566849e-01 4.561209e-01 5.169752e-01 3.264042e-01 3.627485e-02\n [553] 3.949386e-01 3.669585e-01 8.733192e-01 7.346710e-02 2.047444e-01 9.306201e-02\n [559] 3.946652e-01 7.964425e-01 4.469663e-01 9.019875e-01 3.962152e-03 1.621503e-01\n [565] 9.217070e-01 4.003954e-03 7.640834e-01 6.954450e-02 1.755080e-01 8.942880e-01\n [571] 1.792615e-02 1.664076e-01 4.937401e-01 3.133538e-01 6.605614e-01 2.980317e-01\n [577] 3.144637e-01 3.997729e-01 6.706765e-03 5.200022e-01 7.750813e-01 6.253181e-01\n [583] 7.345378e-02 8.382812e-01 4.703207e-01 3.942594e-03 9.244145e-01 7.610229e-01\n [589] 6.767684e-01 6.256713e-01 5.712515e-01 7.953850e-01 2.749314e-01 4.250338e-01\n [595] 7.650676e-01 2.789948e-01 1.287448e-01 2.885587e-01 1.322106e-01 9.571387e-01\n [601] 9.528265e-01 6.466438e-01 9.944465e-01 2.381771e-01 8.802870e-01 9.772657e-01\n [607] 8.972094e-01 1.048052e-01 4.476256e-01 5.929386e-01 5.173140e-01 8.704094e-01\n [613] 1.687019e-01 4.914906e-01 4.420361e-01 5.046681e-01 5.574947e-01 8.110628e-01\n [619] 6.858643e-01 1.190237e-01 4.152257e-01 5.362452e-01 6.465532e-01 7.493356e-01\n [625] 9.684055e-01 8.622536e-01 6.302998e-01 7.845257e-02 2.040127e-02 2.679388e-01\n [631] 5.801192e-01 8.607559e-02 8.869123e-02 8.375751e-01 6.213943e-01 4.668519e-01\n [637] 3.979426e-01 8.514774e-01 2.388480e-01 2.587809e-01 1.398323e-01 3.899975e-01\n [643] 5.275828e-01 5.819829e-01 8.947017e-03 5.825016e-01 8.205341e-01 8.594585e-01\n [649] 4.383700e-01 3.540588e-01 3.999980e-03 8.195700e-02 5.513107e-02 8.804559e-01\n [655] 4.460802e-02 3.610774e-01 9.560762e-01 2.119287e-01 1.100148e-01 5.623630e-01\n [661] 1.541247e-03 4.229121e-01 1.330188e-01 8.216185e-01 2.729476e-01 6.652049e-01\n [667] 8.652747e-01 5.016885e-01 7.600292e-01 6.919445e-01 9.565169e-01 3.066762e-01\n [673] 5.328506e-01 5.318552e-01 1.491969e-01 9.508635e-01 7.185640e-01 6.469556e-01\n [679] 1.384868e-01 5.271885e-01 6.033435e-01 1.933717e-01 4.845026e-01 5.861438e-01\n [685] 9.740393e-01 6.153922e-01 5.912278e-01 8.022706e-01 6.909729e-01 4.882239e-02\n [691] 1.226152e-01 1.896140e-01 2.976366e-03 7.062370e-01 8.247226e-01 3.649172e-01\n [697] 1.756347e-01 6.865414e-01 6.227961e-01 3.062643e-01 3.812476e-02 8.537122e-02\n [703] 9.057651e-01 3.993239e-01 7.227072e-04 2.922580e-01 4.477262e-01 4.324600e-01\n [709] 4.070883e-02 3.960201e-01 1.275019e-01 3.582619e-01 7.378635e-04 3.439765e-01\n [715] 9.332736e-01 9.628696e-01 6.274816e-01 9.717846e-01 4.176568e-04 5.591277e-02\n [721] 6.006515e-01 1.438184e-01 4.783705e-01 7.547687e-01 7.435877e-01 4.962238e-01\n [727] 4.293343e-01 2.258360e-02 3.086084e-01 6.101525e-01 8.760415e-01 7.886039e-01\n [733] 6.799891e-01 2.446572e-01 7.743403e-01 3.164434e-01 9.551240e-01 7.186069e-01\n [739] 6.499554e-01 2.118919e-01 5.113727e-01 4.825803e-01 2.464896e-01 5.680040e-01\n [745] 5.898185e-01 4.185257e-01 9.794990e-02 9.406018e-01 9.687252e-01 4.462771e-01\n [751] 3.800592e-01 7.842664e-01 1.062049e-01 6.597810e-01 2.715464e-01 6.858904e-01\n [757] 9.074613e-01 1.954511e-02 8.951969e-01 4.663258e-01 3.983918e-01 5.161161e-01\n [763] 8.408593e-01 2.901369e-01 7.811782e-01 5.306544e-01 3.213792e-01 3.103223e-01\n [769] 6.238623e-01 3.251497e-01 2.995578e-01 9.611602e-01 6.937948e-01 3.324206e-01\n [775] 7.928994e-01 4.064114e-01 3.131291e-02 5.922049e-01 6.881799e-01 7.761215e-01\n [781] 2.349834e-01 2.215975e-01 3.889381e-01 6.284449e-01 5.596660e-01 9.838749e-01\n [787] 8.635139e-01 9.762162e-01 9.576179e-01 6.910737e-01 9.684219e-01 1.072290e-04\n [793] 8.227578e-01 3.494162e-04 3.599207e-01 5.240435e-01 4.145757e-01 9.604111e-03\n [799] 6.872215e-01 3.109674e-02 4.575313e-01 4.540648e-01 8.626440e-01 1.170019e-01\n [805] 2.478636e-01 7.821157e-01 2.643065e-01 2.225974e-01 8.596173e-02 8.752965e-01\n [811] 9.576422e-01 2.794384e-02 5.700233e-01 9.349886e-01 9.254936e-01 4.100595e-01\n [817] 9.401225e-01 5.997557e-01 2.980548e-01 3.469936e-01 1.641790e-01 6.687500e-01\n [823] 1.377370e-06 1.364465e-01 5.202465e-01 8.946199e-01 2.514533e-01 1.007954e-02\n [829] 2.610219e-02 1.001389e-03 4.790585e-01 1.100708e-01 9.425609e-01 4.353140e-01\n [835] 1.627839e-01 6.095069e-01 3.243661e-01 2.818437e-02 8.555218e-01 4.056604e-01\n [841] 6.050677e-01 6.329992e-01 9.430416e-01 7.529209e-01 2.770969e-01 2.059901e-01\n [847] 2.706809e-01 1.205189e-03 5.987080e-01 8.107302e-01 7.963998e-03 7.753491e-01\n [853] 5.027507e-02 1.550258e-01 8.703869e-02 3.768572e-01 3.299394e-01 1.084049e-01\n [859] 1.367371e-01 8.891284e-01 1.073490e-01 2.090345e-01 3.860939e-01 3.569021e-02\n [865] 9.123115e-01 5.200021e-01 9.628876e-01 7.558977e-01 1.789859e-02 9.013507e-01\n [871] 1.593458e-01 8.622259e-01 5.991593e-03 1.869733e-02 8.489342e-01 5.810539e-01\n [877] 7.386435e-01 9.072509e-01 6.069016e-01 3.981566e-01 6.422527e-01 5.260790e-01\n [883] 3.851367e-01 2.110784e-01 1.215727e-01 9.101522e-01 9.477956e-01 9.769010e-01\n [889] 1.895899e-02 6.548481e-04 4.577718e-01 7.299303e-01 5.909070e-03 1.123766e-01\n [895] 1.876529e-01 2.888938e-01 6.386936e-01 7.309877e-01 9.311258e-01 2.770312e-01\n [901] 9.550502e-01 4.809542e-01 9.486596e-01 1.754825e-01 2.154796e-01 3.618871e-01\n [907] 7.727619e-03 1.539420e-01 1.700089e-01 2.245864e-01 8.669845e-01 1.354407e-01\n [913] 7.958764e-01 8.741414e-01 8.622220e-01 4.649499e-01 2.578447e-01 2.964702e-01\n [919] 4.116013e-01 3.274364e-02 2.409112e-01 1.135170e-01 2.161419e-01 4.196113e-01\n [925] 9.946634e-01 3.705458e-01 1.626570e-02 5.675716e-01 6.590625e-01 4.648076e-03\n [931] 8.017901e-01 1.626664e-01 8.888951e-01 6.788589e-01 5.410045e-01 8.548664e-01\n [937] 9.445873e-01 2.412846e-01 5.212744e-01 7.457951e-01 9.299277e-01 8.027972e-01\n [943] 9.625038e-01 4.555620e-01 9.176344e-01 1.343710e-01 1.726891e-03 6.486595e-01\n [949] 3.847728e-01 1.696002e-01 2.432187e-01 5.855608e-03 2.326925e-01 3.961231e-01\n [955] 4.255313e-02 7.675508e-01 5.448272e-01 3.474887e-01 7.999520e-01 6.672495e-01\n [961] 9.764810e-01 4.959501e-01 2.458427e-02 2.676467e-01 1.715924e-01 3.226133e-01\n [967] 3.091354e-01 3.753057e-01 3.063234e-02 1.024971e-01 5.221563e-02 9.521028e-01\n [973] 2.212798e-01 5.793715e-01 4.244067e-01 8.045358e-01 8.733547e-01 5.956608e-01\n [979] 2.305259e-01 2.423336e-05 9.968167e-01 8.299282e-01 7.409586e-01 9.524011e-01\n [985] 5.593726e-01 5.216346e-01 2.983491e-01 1.282499e-01 1.920044e-02 4.853236e-01\n [991] 3.779592e-01 5.641750e-01 9.515904e-01 7.808364e-01 3.733099e-01 9.352004e-01\n [997] 4.215405e-02 6.725078e-01 6.285389e-01 3.678331e-01\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n\n$adjpval\n   [1] 3.881910e-01 9.740323e-01 3.080634e-01 7.735434e-01 9.973159e-01 8.876529e-01\n   [7] 2.034502e-01 9.740323e-01 8.137830e-01 8.534698e-01 8.933644e-01 9.903281e-01\n  [13] 9.901929e-01 7.958258e-01 9.380258e-01 9.241649e-01 7.768961e-01 7.564512e-01\n  [19] 3.707840e-01 9.264789e-01 9.927259e-01 9.716760e-01 9.901929e-01 9.790059e-01\n  [25] 2.645720e-01 8.916910e-01 6.513966e-01 5.542371e-01 8.862321e-01 8.489865e-01\n  [31] 2.778013e-01 9.740323e-01 9.380258e-01 9.917250e-01 9.740323e-01 9.053352e-01\n  [37] 4.472130e-01 9.817418e-01 9.886271e-01 3.227749e-01 7.294916e-01 7.801626e-01\n  [43] 6.310428e-01 7.761568e-01 3.170895e-01 5.954012e-02 4.765365e-01 9.746053e-01\n  [49] 4.073600e-01 7.870689e-01 2.265070e-01 5.746750e-01 7.977963e-01 3.807592e-01\n  [55] 9.817628e-01 5.410474e-01 7.940452e-01 9.420696e-01 8.563704e-01 7.970690e-01\n  [61] 9.716913e-01 7.829430e-01 8.876148e-01 7.627426e-01 6.811219e-01 3.529406e-01\n  [67] 6.779845e-01 7.427190e-01 7.789806e-01 7.926212e-01 3.198416e-01 4.073600e-01\n  [73] 6.188562e-01 9.424722e-01 4.551328e-01 8.951609e-01 9.929945e-01 9.705585e-01\n  [79] 9.414883e-01 9.740323e-01 9.826470e-01 8.543471e-01 7.427190e-01 8.489865e-01\n  [85] 9.700224e-01 9.496695e-01 6.222018e-01 8.890185e-01 8.372785e-01 8.282292e-01\n  [91] 9.835841e-01 9.905654e-01 9.740323e-01 8.919908e-01 9.484220e-01 4.093319e-01\n  [97] 6.806599e-01 9.716079e-01 9.342185e-01 8.934678e-01 5.172842e-02 9.050572e-01\n [103] 5.662849e-01 9.641410e-01 9.929945e-01 6.829086e-01 3.462012e-01 9.737358e-01\n [109] 8.819970e-01 8.997253e-01 3.306145e-01 4.569189e-01 6.310428e-01 9.886387e-01\n [115] 7.832920e-01 8.047814e-01 6.531155e-01 6.088228e-01 7.293948e-01 9.817418e-01\n [121] 6.189187e-01 3.715780e-01 7.768961e-01 9.683032e-01 9.424722e-01 9.961422e-01\n [127] 2.855872e-03 9.929945e-01 8.781149e-01 7.158195e-01 9.901929e-01 9.682968e-01\n [133] 9.525918e-01 9.790059e-01 9.929945e-01 8.862321e-01 7.283631e-01 7.725756e-01\n [139] 7.866240e-02 1.139090e-01 4.934610e-01 6.513966e-01 9.886387e-01 7.676348e-01\n [145] 1.423324e-01 9.419348e-01 4.647147e-01 9.016588e-01 8.143557e-01 9.112124e-01\n [151] 4.647147e-01 2.884702e-01 5.800885e-01 8.794956e-01 9.741796e-01 2.581130e-01\n [157] 8.319766e-01 1.713949e-01 4.535700e-01 7.860728e-01 7.412281e-01 3.237098e-01\n [163] 5.171759e-01 5.985697e-01 9.886558e-01 7.733700e-01 8.208561e-01 8.622948e-01\n [169] 8.996395e-01 6.224327e-01 5.938730e-01 9.955608e-01 9.929945e-01 7.870689e-01\n [175] 9.886387e-01 9.817418e-01 5.720343e-01 9.934915e-01 9.740323e-01 9.711957e-01\n [181] 8.772002e-01 9.705585e-01 7.789713e-01 9.577250e-01 9.538088e-01 9.864461e-01\n [187] 9.241993e-01 3.728488e-01 6.987101e-01 7.864627e-01 9.572255e-01 7.864627e-01\n [193] 9.543712e-01 9.858353e-01 7.207120e-01 8.352149e-01 9.273869e-01 9.461846e-01\n [199] 9.740323e-01 7.961149e-01 9.290725e-01 8.534698e-01 9.209934e-01 4.611927e-01\n [205] 9.243054e-01 6.885545e-01 9.468673e-01 4.843496e-01 2.161996e-01 5.362353e-01\n [211] 8.819970e-01 9.679595e-01 7.829430e-01 9.853033e-01 8.963212e-01 7.467996e-01\n [217] 9.112124e-01 6.145630e-01 5.426113e-01 7.829430e-01 7.293948e-01 7.124467e-01\n [223] 5.152520e-01 6.624200e-01 9.420696e-01 7.923553e-01 2.972529e-01 9.864461e-01\n [229] 2.281259e-01 8.598716e-01 9.219281e-01 9.956088e-01 9.705585e-01 9.944706e-01\n [235] 2.521019e-01 8.564729e-01 9.206154e-01 8.862321e-01 5.480208e-01 9.886271e-01\n [241] 1.787719e-02 5.751036e-01 8.657462e-01 9.544671e-01 5.103735e-01 9.748664e-01\n [247] 9.320442e-01 8.695671e-01 5.552122e-01 9.674588e-01 9.677018e-01 8.345856e-01\n [253] 8.632395e-01 6.739340e-01 2.448643e-01 7.870689e-01 9.414883e-01 8.317728e-01\n [259] 8.518195e-01 9.178202e-01 9.525918e-01 8.823350e-01 8.349223e-01 8.788927e-01\n [265] 8.997253e-01 9.419348e-01 5.868647e-08 8.564729e-01 9.569287e-01 8.788927e-01\n [271] 9.908317e-01 9.997021e-01 9.817418e-01 9.058674e-01 9.918741e-01 9.120524e-01\n [277] 9.940421e-01 9.686028e-01 8.776898e-01 9.572255e-01 8.788927e-01 9.901929e-01\n [283] 9.740323e-01 9.901929e-01 9.587190e-01 4.618758e-01 8.749927e-01 6.740349e-01\n [289] 5.415967e-01 1.887220e-01 7.639111e-01 1.374275e-06 1.089030e-01 9.663191e-01\n [295] 3.011013e-01 8.866013e-01 9.141196e-01 9.110597e-01 9.709556e-01 8.819970e-01\n [301] 9.909229e-01 9.941432e-01 8.871575e-01 9.336333e-01 9.903281e-01 9.716760e-01\n [307] 7.716004e-01 7.829430e-01 6.226074e-01 4.098103e-01 5.778095e-01 5.561868e-01\n [313] 7.768961e-01 6.504858e-01 9.538088e-01 3.412867e-01 9.582231e-01 3.293551e-01\n [319] 8.208561e-01 9.817418e-01 9.740323e-01 9.955608e-01 9.956088e-01 9.817418e-01\n [325] 9.011593e-01 9.440989e-01 5.516853e-01 9.717364e-01 9.414883e-01 9.296327e-01\n [331] 9.058674e-01 9.956088e-01 9.484220e-01 9.739266e-01 7.923553e-01 7.598418e-01\n [337] 2.285548e-01 1.052838e-01 5.845768e-01 2.694464e-01 7.656822e-01 9.168632e-01\n [343] 9.886387e-01 5.561868e-01 5.129189e-01 9.241993e-01 9.835156e-01 9.740323e-01\n [349] 8.768774e-01 1.139090e-01 8.619097e-01 9.380258e-01 8.534698e-01 4.121597e-02\n [355] 7.913524e-01 5.326052e-01 8.319766e-01 5.104576e-01 7.958258e-01 9.883738e-01\n [361] 7.250069e-01 8.576653e-01 8.632395e-01 9.929945e-01 8.627526e-01 6.225051e-01\n [367] 3.880232e-03 5.675692e-01 9.883738e-01 7.731071e-01 4.883858e-01 9.918741e-01\n [373] 9.927259e-01 9.677018e-01 9.364754e-01 9.934231e-01 8.083064e-01 8.448571e-01\n [379] 6.992375e-01 9.289447e-01 9.685520e-01 9.837797e-01 6.542124e-01 1.292465e-01\n [385] 7.768961e-01 7.672656e-01 2.108809e-01 4.370878e-01 9.422738e-01 9.817418e-01\n [391] 9.905654e-01 7.901321e-01 9.181048e-01 9.935096e-01 9.424722e-01 7.851727e-01\n [397] 9.271564e-01 9.290212e-01 8.835113e-01 8.234285e-01 6.520816e-01 8.269339e-01\n [403] 8.997253e-01 8.663293e-01 5.664046e-01 1.020327e-02 8.833160e-01 3.189505e-01\n [409] 9.751540e-01 8.518195e-01 8.052168e-01 7.281874e-01 9.464666e-01 9.817418e-01\n [415] 2.838427e-01 9.184535e-01 7.801576e-01 1.465841e-01 2.884702e-01 9.683032e-01\n [421] 8.145249e-01 2.747088e-01 4.647147e-01 6.866859e-01 9.025530e-01 8.519638e-01\n [427] 9.995262e-01 8.518195e-01 9.790059e-01 9.543712e-01 3.675597e-01 9.716913e-01\n [433] 6.814244e-01 9.099109e-01 9.682968e-01 9.136427e-01 9.266241e-01 7.812734e-01\n [439] 6.959817e-01 9.136427e-01 8.797282e-01 8.876529e-01 5.286238e-02 9.312794e-01\n [445] 5.664046e-01 9.420696e-01 1.767400e-01 7.870689e-01 7.450211e-01 8.860778e-01\n [451] 9.572255e-01 9.955608e-01 8.996395e-01 8.919908e-01 4.536956e-01 3.275769e-01\n [457] 9.845266e-01 8.599679e-01 9.290212e-01 9.680082e-01 6.682988e-01 8.806842e-01\n [463] 9.736782e-01 7.779246e-01 6.319525e-01 9.025530e-01 4.702212e-01 7.500629e-01\n [469] 8.862321e-01 3.691163e-01 8.952313e-01 2.453077e-01 7.980299e-01 6.644158e-01\n [475] 4.051600e-01 7.308358e-01 9.636021e-01 9.740323e-01 6.289974e-01 9.560453e-01\n [481] 9.883738e-01 9.961422e-01 7.048834e-01 9.414883e-01 5.756847e-01 8.942196e-01\n [487] 4.337041e-01 8.489865e-01 7.639111e-01 6.680559e-01 9.168187e-01 9.500900e-01\n [493] 8.907758e-01 9.817628e-01 9.027872e-01 1.979313e-01 7.427190e-01 9.061592e-01\n [499] 7.848058e-01 3.691163e-01 3.431078e-01 5.277287e-01 8.876529e-01 9.462263e-01\n [505] 3.483429e-01 8.695671e-01 7.919192e-01 5.985697e-01 9.680082e-01 8.793449e-01\n [511] 9.883146e-01 9.912386e-01 8.934678e-01 8.876148e-01 9.794744e-01 8.564729e-01\n [517] 5.938730e-01 9.912386e-01 9.027872e-01 5.129189e-01 7.848058e-01 7.870689e-01\n [523] 5.822956e-01 9.641410e-01 6.921489e-01 2.106664e-01 7.207120e-01 1.139090e-01\n [529] 8.619097e-01 9.737358e-01 7.801576e-01 8.133132e-01 4.217089e-01 2.117538e-01\n [535] 8.559638e-01 8.189831e-01 7.220847e-01 9.737358e-01 8.008666e-01 7.870689e-01\n [541] 8.733824e-01 3.456737e-01 8.241965e-01 6.537884e-01 1.708130e-01 8.483849e-01\n [547] 1.985508e-01 9.110597e-01 8.725291e-01 8.945015e-01 7.948846e-01 3.886897e-01\n [553] 8.457424e-01 8.246496e-01 9.817628e-01 5.090806e-01 7.124467e-01 5.572142e-01\n [559] 8.455986e-01 9.737165e-01 8.687115e-01 9.883146e-01 1.441783e-01 6.570066e-01\n [565] 9.905654e-01 1.441783e-01 9.687831e-01 4.958820e-01 6.744715e-01 9.858353e-01\n [571] 2.888654e-01 6.644453e-01 8.874936e-01 7.870689e-01 9.407311e-01 7.811752e-01\n [577] 7.870689e-01 8.471692e-01 1.824834e-01 8.957667e-01 9.705585e-01 9.292231e-01\n [583] 5.090806e-01 9.756837e-01 8.797282e-01 1.441783e-01 9.908317e-01 9.685520e-01\n [589] 9.426809e-01 9.293261e-01 9.157797e-01 9.735768e-01 7.696961e-01 8.581185e-01\n [595] 9.692836e-01 7.704972e-01 6.113484e-01 7.768961e-01 6.153692e-01 9.929945e-01\n [601] 9.929945e-01 9.355040e-01 9.981614e-01 7.410400e-01 9.835156e-01 9.956088e-01\n [607] 9.863873e-01 5.751800e-01 8.687115e-01 9.203961e-01 8.945380e-01 9.817628e-01\n [613] 6.680559e-01 8.868893e-01 8.657462e-01 8.912371e-01 9.110597e-01 9.740323e-01\n [619] 9.461846e-01 5.967722e-01 8.524788e-01 9.027872e-01 9.355040e-01 9.675373e-01\n [625] 9.948082e-01 9.817418e-01 9.306212e-01 5.185946e-01 3.055673e-01 7.639111e-01\n [631] 9.172242e-01 5.419079e-01 5.464161e-01 9.753887e-01 9.278073e-01 8.788927e-01\n [637] 8.470370e-01 9.790059e-01 7.410400e-01 7.577438e-01 6.254501e-01 8.422301e-01\n [643] 8.996395e-01 9.180327e-01 2.108809e-01 9.181048e-01 9.740323e-01 9.817418e-01\n [649] 8.642717e-01 8.145412e-01 1.441783e-01 5.306222e-01 4.543293e-01 9.835156e-01\n [655] 4.205480e-01 8.205842e-01 9.929945e-01 7.202227e-01 5.841338e-01 9.120524e-01\n [661] 8.718186e-02 8.566152e-01 6.162154e-01 9.740323e-01 7.672656e-01 9.416862e-01\n [667] 9.817418e-01 8.890185e-01 9.685520e-01 9.484220e-01 9.929945e-01 7.855418e-01\n [673] 9.004818e-01 9.002424e-01 6.414552e-01 9.929945e-01 9.572255e-01 9.355040e-01\n [679] 6.231906e-01 8.996395e-01 9.235210e-01 6.987101e-01 8.862048e-01 9.186409e-01\n [685] 9.956088e-01 9.263951e-01 9.203961e-01 9.739266e-01 9.480296e-01 4.320778e-01\n [691] 6.010524e-01 6.951257e-01 1.271066e-01 9.539526e-01 9.741796e-01 8.234285e-01\n [697] 6.747574e-01 9.461846e-01 9.278629e-01 7.853400e-01 3.952533e-01 5.410474e-01\n [703] 9.886271e-01 8.470370e-01 5.356756e-02 7.783716e-01 8.687115e-01 8.619126e-01\n [709] 4.051600e-01 8.462094e-01 6.101865e-01 8.176434e-01 5.406947e-02 8.062784e-01\n [715] 9.912949e-01 9.936090e-01 9.296327e-01 9.955608e-01 3.793324e-02 4.569189e-01\n [721] 9.227387e-01 6.310428e-01 8.819970e-01 9.680082e-01 9.655710e-01 8.876529e-01\n [727] 8.611391e-01 3.197153e-01 7.864627e-01 9.243252e-01 9.826470e-01 9.717364e-01\n [733] 9.440989e-01 7.431206e-01 9.705585e-01 7.870689e-01 9.929945e-01 9.572255e-01\n [739] 9.370702e-01 7.202227e-01 8.919908e-01 8.844448e-01 7.450211e-01 9.136427e-01\n [745] 9.200398e-01 8.539354e-01 5.635917e-01 9.924684e-01 9.948082e-01 8.687115e-01\n [751] 8.345363e-01 9.716760e-01 5.783415e-01 9.403465e-01 7.662653e-01 9.461846e-01\n [757] 9.886271e-01 2.983115e-01 9.858353e-01 8.786273e-01 8.470370e-01 8.942196e-01\n [763] 9.765448e-01 7.768961e-01 9.711957e-01 8.997875e-01 7.913614e-01 7.870689e-01\n [769] 9.284506e-01 7.940666e-01 7.812734e-01 9.935678e-01 9.489601e-01 7.970690e-01\n [775] 9.726751e-01 8.489865e-01 3.687524e-01 9.203961e-01 9.464666e-01 9.705585e-01\n [781] 7.380860e-01 7.281874e-01 8.417599e-01 9.296327e-01 9.112124e-01 9.956088e-01\n [787] 9.817418e-01 9.956088e-01 9.929945e-01 9.480669e-01 9.948082e-01 1.646348e-02\n [793] 9.740323e-01 3.338089e-02 8.189831e-01 8.972378e-01 8.524788e-01 2.161996e-01\n [799] 9.461846e-01 3.686164e-01 8.734134e-01 8.714676e-01 9.817418e-01 5.950211e-01\n [805] 7.463687e-01 9.714683e-01 7.602053e-01 7.283631e-01 5.415967e-01 9.824216e-01\n [811] 9.929945e-01 3.478685e-01 9.147182e-01 9.915118e-01 9.908317e-01 8.516163e-01\n [817] 9.922497e-01 9.223764e-01 7.811752e-01 8.083064e-01 6.608664e-01 9.420696e-01\n [823] 5.383013e-04 6.224327e-01 8.958103e-01 9.858353e-01 7.501720e-01 2.204215e-01\n [829] 3.396972e-01 6.518957e-02 8.819970e-01 5.841670e-01 9.927259e-01 8.632395e-01\n [835] 6.578902e-01 9.243054e-01 7.938396e-01 3.483429e-01 9.806966e-01 8.489865e-01\n [841] 9.235210e-01 9.313175e-01 9.927259e-01 9.680082e-01 7.700164e-01 7.133761e-01\n [847] 7.655639e-01 7.195985e-02 9.222493e-01 9.740323e-01 1.985760e-01 9.705585e-01\n [853] 4.377327e-01 6.504858e-01 5.426113e-01 8.326357e-01 7.962750e-01 5.815412e-01\n [859] 6.224327e-01 9.848202e-01 5.800086e-01 7.168843e-01 8.400804e-01 3.864791e-01\n [865] 9.886387e-01 8.957667e-01 9.936090e-01 9.682968e-01 2.888654e-01 9.882445e-01\n [871] 6.551109e-01 9.817418e-01 1.708130e-01 2.937144e-01 9.790059e-01 9.178202e-01\n [877] 9.632240e-01 9.886271e-01 9.239569e-01 8.470370e-01 9.342582e-01 8.991864e-01\n [883] 8.388968e-01 7.188482e-01 5.996139e-01 9.886387e-01 9.929945e-01 9.956088e-01\n [889] 2.948048e-01 5.172842e-02 8.737433e-01 9.596240e-01 1.708130e-01 5.869601e-01\n [895] 6.926615e-01 7.768961e-01 9.335083e-01 9.599180e-01 9.912386e-01 7.700164e-01\n [901] 9.929945e-01 8.832217e-01 9.929945e-01 6.744715e-01 7.220847e-01 8.209777e-01\n [907] 1.965742e-01 6.494144e-01 6.694977e-01 7.293948e-01 9.817418e-01 6.203047e-01\n [913] 9.736782e-01 9.817628e-01 9.817418e-01 8.781149e-01 7.569788e-01 7.801626e-01\n [919] 8.518195e-01 3.717383e-01 7.417160e-01 5.898430e-01 7.227333e-01 8.549332e-01\n [925] 9.982188e-01 8.272917e-01 2.753112e-01 9.136427e-01 9.402198e-01 1.513608e-01\n [931] 9.739266e-01 6.578902e-01 9.848202e-01 9.438179e-01 9.054179e-01 9.804932e-01\n [937] 9.927739e-01 7.419761e-01 8.963745e-01 9.663939e-01 9.912386e-01 9.739266e-01\n [943] 9.935678e-01 8.721067e-01 9.901929e-01 6.188562e-01 9.290601e-02 9.367105e-01\n [949] 8.387073e-01 6.694423e-01 7.427190e-01 1.705592e-01 7.352845e-01 8.462480e-01\n [955] 4.123273e-01 9.697966e-01 9.064067e-01 8.085083e-01 9.739266e-01 9.419348e-01\n [961] 9.956088e-01 8.876529e-01 3.320035e-01 7.637155e-01 6.723843e-01 7.919192e-01\n [967] 7.866848e-01 8.316849e-01 3.661402e-01 5.705245e-01 4.429301e-01 9.929945e-01\n [973] 7.281874e-01 9.172242e-01 8.576653e-01 9.740323e-01 9.817628e-01 9.209934e-01\n [979] 7.319365e-01 5.452769e-03 9.988402e-01 9.748664e-01 9.641410e-01 9.929945e-01\n [985] 9.112058e-01 8.963745e-01 7.811752e-01 6.103465e-01 2.961495e-01 8.862321e-01\n [991] 8.328330e-01 9.127771e-01 9.929945e-01 9.711957e-01 8.301368e-01 9.915118e-01\n [997] 4.103101e-01 9.424283e-01 9.296327e-01 8.250173e-01\n [ reached getOption(\"max.print\") -- omitted 11897 entries ]\n"} -->

```
$DEindices
  [1]   127   241   267   292   354   367   406   719   792   794   823   980  1286  1428  1443
 [16]  1566  1643  1889  1895  1919  2013  2096  2215  2224  2226  2227  2231  2390  2449  2710
 [31]  2770  2848  2913  3000  3066  3115  3144  3328  3365  3391  3513  3514  3670  3697  3704
 [46]  3705  3706  3708  3712  3716  3718  3822  3998  4006  4033  4078  4087  4097  4269  4270
 [61]  4600  4673  4716  4776  4882  5004  5240  5253  5296  5310  5333  5402  5414  5465  5566
 [76]  5578  5638  5752  5758  5791  5800  5901  6184  6185  6215  6234  6331  6339  6695  6751
 [91]  6801  6892  6908  6919  7305  7508  7571  7627  7671  7673  8452  8517  8636  8701  8723
[106]  8781  8789  8814  8875  8909  9043  9191  9256  9365  9393  9425  9537  9578  9903  9922
[121]  9943  9949  9951 10001 10092 10295 10324 10383 10458 10504 10562 10564 10666 10763 10843
[136] 11107 11147 11228 11256 11295 11330 11331 11398 11481 11563 11873 11937 11999 12359 12439
[151] 12611 12653 12701 12749 12816

$TestStatistic
   [1]  1.799514205 -0.867698432  2.037192675  0.571433901 -2.385440124  0.009036393
   [7]  2.394161699 -0.908478865  0.379173135  0.209522574 -0.035353240 -1.410954957
  [13] -1.389507873  0.444954783 -0.394559880 -0.274516127  0.554755539  0.653170841
  [19]  1.853807683 -0.297206620 -1.575411103 -0.785531599 -1.389572176 -1.036129130
  [25]  2.173925370 -0.013212432  1.008171535  1.334242858  0.031221936  0.236373836
  [31]  2.131568295 -0.884401602 -0.392739672 -1.529063888 -0.919470618 -0.100327943
  [37]  1.616258596 -1.091203220 -1.334020440  1.991074386  0.749191998  0.535593081
  [43]  1.063919862  0.564043744  2.011198390  3.130503416  1.542436171 -0.941545524
  [49]  1.733612134  0.494569652  2.304121579  1.258087871  0.430328890  1.818190982
  [55] -1.144177620  1.368231538  0.454360268 -0.435967962  0.199435322  0.432595537
  [61] -0.799033252  0.521501056  0.014425041  0.623104260  0.913412282  1.899311376
  [67]  0.922076200  0.695025385  0.543879642  0.458066378  2.000623047  1.734424620
  [73]  1.105587334 -0.452160768  1.594878247 -0.044774594 -1.610628794 -0.745308432
  [79] -0.422101661 -0.907460769 -1.155513073  0.204471690  0.695761078  0.242127642
  [85] -0.739807162 -0.513343546  1.097144750 -0.004088184  0.296698460  0.328443062
  [91] -1.182464517 -1.424267397 -0.875115261 -0.024368032 -0.502765924  1.730528898
  [97]  0.915285063 -0.780855525 -0.364058652 -0.036677596  3.218096185 -0.098423977
 [103]  1.284968570 -0.643678740 -1.651362843  0.907798015  1.917244091 -0.832221925
 [109]  0.057963428 -0.071907285  1.972498991  1.589676329  1.062886981 -1.352287370
 [115]  0.517867691  0.409898352  1.002923374  1.140791800  0.755688353 -1.105228719
 [121]  1.105302986  1.846006508  0.556539907 -0.699376712 -0.452884570 -2.244520205
 [127]  4.251251310 -1.610077664  0.090096485  0.812327093 -1.383150041 -0.692496725
 [133] -0.529678495 -1.035676810 -1.710087720  0.032481960  0.763165137  0.578608429
 [139]  2.997697166  2.824714898  1.486745190  1.008100648 -1.349417970  0.602809350
 [145]  2.679550670 -0.432224706  1.568269458 -0.087133123  0.375446716 -0.150781069
 [151]  1.568883797  2.101368464  1.239940167  0.077900214 -0.934680653  2.197023318
 [157]  0.315326608  2.509405375  1.603456389  0.503831968  0.708466342  1.988689455
 [163]  1.417879561  1.171091751 -1.361057209  0.576153360  0.354060533  0.166822902
 [169] -0.069320378  1.092582157  1.198190596 -1.929647803 -1.679197474  0.491508082
 [175] -1.348336010 -1.109755655  1.264696171 -1.750452896 -0.871494042 -0.768263266
 [181]  0.094460673 -0.747997174  0.544764296 -0.584005091 -0.541802660 -1.269089813
 [187] -0.276299966  1.836964384  0.866330879  0.500755962 -0.580244695  0.500527187
 [193] -0.549190288 -1.257913461  0.795578563  0.301849879 -0.307894072 -0.484856605
 [199] -0.908331662  0.442093686 -0.318830896  0.209433613 -0.242237114  1.579762409
 [205] -0.278324693  0.894505078 -0.494750481  1.519406102  2.340974907  1.380278962
 [211]  0.054987144 -0.676397220  0.521345279 -1.230469430 -0.052040353  0.678764051
 [217] -0.152034143  1.118348500  1.360906987  0.520035246  0.760559905  0.824496619
 [223]  1.427296729  0.973746626 -0.440924074  0.458681065  2.065186022 -1.269279292
 [229]  2.295719118  0.184864002 -0.246091391 -2.041187327 -0.753192073 -1.819332552
 [235]  2.220440328  0.197162248 -0.237219101  0.029842367  1.344320456 -1.318854527
 [241]  3.672706415  1.254959595  0.147213404 -0.549777892  1.448317444 -0.946011338
 [247] -0.344413813  0.126332378  1.331531976 -0.667636259 -0.675175371  0.304666385
 [253]  0.161819043  0.940646314  2.243977112  0.480856397 -0.422680195  0.316180698
 [259]  0.224377104 -0.204715431 -0.530315932  0.049829169  0.304093614  0.080937054
 [265] -0.074827368 -0.431243908  6.660431903  0.197363960 -0.569711528  0.080041362
 [271] -1.438380006 -3.045227123 -1.090077404 -0.109979888 -1.532888844 -0.157680852
 [277] -1.796954253 -0.714009882  0.092628351 -0.579197728  0.081228670 -1.385179082
 [283] -0.862804087 -1.399451882 -0.594016448  1.578471914  0.102005227  0.937159808
 [289]  1.366447935  2.451103375  0.618628000  6.070051394  2.846182221 -0.659588277
 [295]  2.057454204  0.026150494 -0.174401341 -0.140826691 -0.765225619  0.051900382
 [301] -1.451250709 -1.802079624  0.019830480 -0.359481231 -1.411121715 -0.788083133
 [307]  0.580538908  0.520878532  1.091234743  1.727405543  1.248078324  1.327653406
 [313]  0.556942367  1.015582086 -0.540345247  1.935474628 -0.586445906  1.975837330
 [319]  0.354159183 -1.071999591 -0.913448506 -1.903139549 -2.123262012 -1.108840166
 [325] -0.085086984 -0.467252521  1.338380508 -0.801984469 -0.420760362 -0.329665064
 [331] -0.108240860 -2.035729816 -0.501758046 -0.849921994  0.458555714  0.636648449
 [337]  2.292646119  2.866314611  1.222006973  2.152750858  0.609784147 -0.188900268
 [343] -1.349033795  1.328117497  1.436523172 -0.277143803 -1.175003131 -0.901318638
 [349]  0.096659271  2.821024362  0.170534862 -0.394494970  0.209099272  3.303696534
 [355]  0.464128314  1.387292752  0.314918273  1.447470387  0.444961818 -1.308731798
 [361]  0.781823513  0.190023555  0.162705076 -1.634644211  0.164950712  1.091557033
 [367]  4.155249881  1.279378544 -1.295183476  0.577147118  1.505049314 -1.541265983
 [373] -1.587364277 -0.675295294 -0.381069227 -1.747042652  0.394586621  0.270981357
 [379]  0.861872906 -0.316994745 -0.705267407 -1.196515011  1.000984668  2.740578109
 [385]  0.552796490  0.604088306  2.366176419  1.646608611 -0.443878974 -1.079327997
 [391] -1.423610071  0.466625561 -0.209590767 -1.752062816 -0.457056457  0.509870786
 [397] -0.303544053 -0.318340827  0.046673883  0.346625158  1.006270621  0.331442818
 [403] -0.075668648  0.142673067  1.281893436  3.880062022  0.047458219  2.005394103
 [409] -0.973085073  0.225024328  0.405775595  0.765902118 -0.490809171 -1.082610157
 [415]  2.113041070 -0.212424979  0.537727564  2.627855197  2.102003192 -0.700842539
 [421]  0.374950362  2.139733389  1.568530111  0.898423309 -0.090022567  0.218983089
 [427] -2.940898765  0.222094591 -1.041126666 -0.547560253  1.868637988 -0.796007864
 [433]  0.912306922 -0.129660971 -0.692423839 -0.167855478 -0.298966712  0.527358933
 [439]  0.874411299 -0.171522746  0.074753084  0.005564998  3.199322325 -0.336327005
 [445]  1.281863807 -0.434680529  2.494663924  0.484561677  0.686276898  0.039425908
 [451] -0.578490261 -1.916314096 -0.070233547 -0.031673116  1.598027805  1.980029612
 [457] -1.214814278  0.184484608 -0.318471785 -0.687720678  0.958824585  0.069382527
 [463] -0.827541648  0.549306698  1.060660976 -0.089884863  1.557364850  0.670585636
 [469]  0.029623845  1.859530175 -0.045635421  2.240990705  0.429709186  0.969104656
 [475]  1.742601642  0.744097675 -0.641210569 -0.869357608  1.069117514 -0.559819296
 [481] -1.297885102 -2.242153954  0.847949371 -0.423575421  1.252185615 -0.040789205
 [487]  1.653008480  0.236936742  0.616715089  0.959273822 -0.187811397 -0.516387348
 [493] -0.010606410 -1.133204616 -0.092154901  2.417005676  0.692423115 -0.111185852
 [499]  0.513013870  1.858104367  1.931453413  1.396139960  0.010937806 -0.488826706
 [505]  1.907962581  0.125769823  0.460532951  1.171536988 -0.677939274  0.078562369
 [511] -1.292482134 -1.491995560 -0.036726247  0.014246223 -1.046855608  0.196269066
 [517]  1.193897362 -1.470890361 -0.091872078  1.436398733  0.512823351  0.491723868
 [523]  1.231834931 -0.648946976  0.888306131  2.370938894  0.796198529  2.821276723
 [529]  0.173660442 -0.832948882  0.538180183  0.380570532  1.691452833  2.359854493
 [535]  0.200914718  0.359097541  0.786912670 -0.831375660  0.419981496  0.491911967
 [541]  0.107071279  1.920155930  0.341651631  1.001619317  2.513821732  0.244462969
 [547]  2.412191005 -0.142569403  0.110211308 -0.042563466  0.449864235  1.795653128
 [553]  0.266470186  0.339919632 -1.142222353  1.450445978  0.824793474  1.322132567
 [559]  0.267180187 -0.828981241  0.133329677 -1.292959704  2.655278144  0.985658847
 [565] -1.416647880  2.651736224 -0.719499478  1.479192064  0.932620516 -1.249659486
 [571]  2.098598520  0.968459137  0.015691811  0.486366213 -0.413995812  0.530069861
 [577]  0.483237078  0.253934894  2.472597005 -0.050159016 -0.755686123 -0.319478344
 [583]  1.450541587 -0.987418496  0.074463752  2.656946856 -1.435407505 -0.709596731
 [589] -0.458681095 -0.320410180 -0.179561363 -0.825249435  0.597965606  0.189032225
 [595] -0.722699205  0.585830094  1.132344373  0.557600302  1.116002100 -1.718406050
 [601] -1.672900244 -0.376275023 -2.539311550  0.712178514 -1.176422663 -2.000294208
 [607] -1.265809687  1.254637299  0.131662442 -0.235110661 -0.043413377 -1.128328480
 [613]  0.959307486  0.021331615  0.145809085 -0.011701488 -0.144620316 -0.881819702
 [619] -0.484161249  1.179881300  0.214122736 -0.090978519 -0.376031402 -0.672400563
 [625] -1.857859540 -1.090500372 -0.332647459  1.415557337  2.045530933  0.619058959
 [631] -0.202198543  1.365324167  1.348858394 -0.984540507 -0.309144737  0.083185719
 [637]  0.258676134 -1.042790616  0.710013033  0.647108537  1.081073224  0.279325459
 [643] -0.069194943 -0.206968818  2.367803544 -0.208297383 -0.917402382 -1.077889866
 [649]  0.155103120  0.374385512  2.652071494  1.392027709  1.597016006 -1.177269078
 [655]  1.699547694  0.355580436 -1.706862140  0.799746856  1.226449354 -0.156963054
 [661]  2.959389317  0.194449237  1.112233824 -0.921550817  0.603922486 -0.426710501
 [667] -1.104328632 -0.004232408 -0.706396382 -0.501369506 -1.711622499  0.505293885
 [673] -0.082437400 -0.079934170  1.039883804 -1.653284633 -0.578581004 -0.377114212
 [679]  1.087143088 -0.068204378 -0.262010827  0.865538371  0.038856050 -0.217636323
 [685] -1.943784308 -0.293401108 -0.230704348 -0.849759390 -0.498610070  1.656380586
 [691]  1.162012276  0.879319442  2.750373731 -0.542424787 -0.933513687  0.345345902
 [697]  0.932129936 -0.486070408 -0.312832584  0.506467244  1.772874384  1.369822131
 [703] -1.315119529  0.255097386  3.185425252  0.546800259  0.131408115  0.170114680
 [709]  1.742519108  0.263662188  1.138279531  0.363108672  3.179414872  0.401634561
 [715] -1.500624228 -1.785002826 -0.325190609 -1.907693353  3.340820011  1.590041128
 [721] -0.255033847  1.063320094  0.054243644 -0.689573196 -0.654445909  0.009465723
 [727]  0.178069270  2.003094056  0.499798595 -0.279716594 -1.155423600 -0.801586580
 [733] -0.467668237  0.691399703 -0.753217349  0.477667627 -1.696707747 -0.578707968
 [739] -0.385199994  0.799873980 -0.028511103  0.043678673  0.685578232 -0.171294665
 [745] -0.227078039  0.205666569  1.293321758 -1.559845669 -1.862379932  0.135072939
 [751]  0.305325413 -0.786683324  1.246966391 -0.411865559  0.608142680 -0.484234905
 [757] -1.325282761  2.063236004 -1.254649000  0.084509143  0.257512011 -0.040407945
 [763] -0.997995878  0.552984733 -0.776178482 -0.076914950  0.463845479  0.494937102
 [769] -0.315640573  0.453346233  0.525672725 -1.764311199 -0.506635777  0.433238989
 [775] -0.816522753  0.236786062  1.861838742 -0.233220568 -0.490697792 -0.759159648
 [781]  0.722533015  0.766809213  0.282087681 -0.327737497 -0.150122424 -2.141294534
 [787] -1.096243650 -1.981211388 -1.723688211 -0.498896007 -1.858089730  3.701346926
 [793] -0.925926164  3.390036843  0.358670667 -0.060304583  0.215789980  2.341465104
 [799] -0.487990029  1.864913906  0.106655159  0.115398198 -1.092275631  1.190108326
 [805]  0.681228008 -0.779358585  0.630124819  0.763450499  1.366049375 -1.151790677
 [811] -1.723957567  1.911910395 -0.176433439 -1.514012078 -1.443027270  0.227391983
 [817] -1.555802479 -0.252714697  0.530003358  0.393449844  0.977426767 -0.436464208
 [823]  4.688307377  1.096424769 -0.050772192 -1.251477843  0.669923390  2.323373973
 [829]  1.941444537  3.089820193  0.052516695  1.226151601 -1.576640899  0.162860792
 [835]  0.983080597 -0.278034064  0.455524154  1.908174004 -1.060413660  0.238722501
 [841] -0.266486386 -0.339807442 -1.580830096 -0.683710087  0.591487400  0.820413958
 [847]  0.610754977  3.034370904 -0.250004449 -0.880590381  2.410561273 -0.756579659
 [853]  1.642192426  1.015113726  1.359218427  0.313745434  0.440080545  1.235055662
 [859]  1.095096919 -1.221906140  1.240750138  0.809775840  0.289514400  1.803049628
 [865] -1.355127318 -0.050158974 -1.785225621 -0.693167144  2.099223825 -1.289286150
 [871]  0.997150226 -1.090374609  2.512639099  2.081428884 -1.031873230 -0.204590262
 [877] -0.639168918 -1.324014701 -0.271252490  0.258121467 -0.364486794 -0.065416976
 [883]  0.292017251  0.802685048  1.167160758 -1.341692663 -1.623845015 -1.993580543
 [889]  2.075740295  3.213846590  0.106048723 -0.612602324  2.517528550  1.213985669
 [895]  0.886578723  0.556619109 -0.354969149 -0.615802985 -1.484228060  0.591683708
 [901] -1.695927620  0.047758946 -1.631993542  0.932719168  0.787551443  0.353419078
 [907]  2.421532029  1.019672219  0.954130035  0.756794758 -1.112249260  1.101035278
 [913] -0.826982205 -1.146188407 -1.090356891  0.087970853  0.650004353  0.534580022
 [919]  0.223427823  1.841917102  0.703374521  1.208034659  0.785289481  0.202887892
 [925] -2.553222597  0.330408023  2.137819558 -0.170194883 -0.409905785  2.600966565
 [931] -0.848032597  0.983558154 -1.220673215 -0.464510380 -0.102964768 -1.057535693
 [937] -1.594494227  0.702176283 -0.053352298 -0.661315678 -1.475252836 -0.851655177
 [943] -1.780511397  0.111620975 -1.389334311  1.105964353  2.924168151 -0.381703904
 [949]  0.292969288  0.955746339  0.695986200  2.520728542  0.730008418  0.263395011
 [955]  1.721797237 -0.730804918 -0.112602594  0.392109395 -0.841449818 -0.432330779
 [961] -1.985958224  0.010151702  1.967127255  0.619945839  0.947891231  0.460403550
 [967]  0.498302675  0.317833278  1.871580388  1.267450550  1.623740263 -1.665593038
 [973]  0.767878074 -0.200285988  0.190632538 -0.857934986 -1.142393450 -0.242131411
 [979]  0.737116057  4.062900402 -2.728276329 -0.953881480 -0.646303520 -1.668594045
 [985] -0.149378771 -0.054256598  0.529154580  1.134702965  2.070549893  0.036796457
 [991]  0.310845148 -0.161562885 -1.660473952 -0.775021136  0.323099546 -1.515684428
 [997]  1.726218509 -0.446848689 -0.327986055  0.337597987
 [ reached getOption("max.print") -- omitted 11897 entries ]

$rawpval
   [1] 3.596869e-02 8.072203e-01 2.081537e-02 2.838528e-01 9.914707e-01 4.963950e-01
   [7] 8.329201e-03 8.181874e-01 3.522796e-01 4.170202e-01 5.141010e-01 9.208711e-01
  [13] 9.176608e-01 3.281762e-01 6.534161e-01 6.081560e-01 2.895309e-01 2.568231e-01
  [19] 3.188334e-02 6.168456e-01 9.424192e-01 7.839290e-01 9.176706e-01 8.499290e-01
  [25] 1.485537e-02 5.052708e-01 1.566861e-01 9.106214e-02 4.875463e-01 4.065713e-01
  [31] 1.652118e-02 8.117603e-01 6.527441e-01 9.368757e-01 8.210753e-01 5.399580e-01
  [37] 5.301921e-02 8.624083e-01 9.089014e-01 2.323635e-02 2.268707e-01 2.961199e-01
  [43] 1.436825e-01 2.863622e-01 2.215225e-02 8.725348e-04 6.148382e-02 8.267873e-01
  [49] 4.149346e-02 3.104520e-01 1.060791e-02 1.041800e-01 3.334782e-01 3.451747e-02
  [55] 8.737250e-01 8.561981e-02 3.247848e-01 6.685700e-01 4.209611e-01 3.326543e-01
  [61] 7.878644e-01 3.010089e-01 4.942454e-01 2.666080e-01 1.805129e-01 2.876177e-02
  [67] 1.782444e-01 2.435197e-01 2.932621e-01 3.234524e-01 2.271651e-02 4.142138e-02
  [73] 1.344526e-01 6.744234e-01 5.536973e-02 5.178565e-01 9.463697e-01 7.719574e-01
  [79] 6.635246e-01 8.179184e-01 8.760598e-01 4.189925e-01 2.432892e-01 4.043406e-01
  [85] 7.702915e-01 6.961445e-01 1.362891e-01 5.016309e-01 3.833484e-01 3.712883e-01
  [91] 8.814893e-01 9.228155e-01 8.092444e-01 5.097205e-01 6.924356e-01 4.176791e-02
  [97] 1.800210e-01 7.825563e-01 6.420929e-01 5.146290e-01 6.452227e-04 5.392022e-01
 [103] 9.940163e-02 7.401081e-01 9.506677e-01 1.819925e-01 2.760346e-02 7.973582e-01
 [109] 4.768889e-01 5.286622e-01 2.427634e-02 5.595389e-02 1.439166e-01 9.118583e-01
 [115] 3.022753e-01 3.409403e-01 1.579489e-01 1.269783e-01 2.249180e-01 8.654698e-01
 [121] 1.345142e-01 3.244563e-02 2.889209e-01 7.578417e-01 6.746841e-01 9.876005e-01
 [127] 1.062897e-05 9.463095e-01 4.641053e-01 2.083020e-01 9.166906e-01 7.556873e-01
 [133] 7.018326e-01 8.498235e-01 9.563752e-01 4.870439e-01 2.226825e-01 2.814267e-01
 [139] 1.360139e-03 2.366135e-03 6.854106e-02 1.567031e-01 9.113986e-01 2.733178e-01
 [145] 3.686052e-03 6.672109e-01 5.840913e-02 5.347172e-01 3.536641e-01 5.599258e-01
 [151] 5.833751e-02 1.780432e-02 1.074988e-01 4.689537e-01 8.250235e-01 1.400939e-02
 [157] 3.762568e-01 6.046731e-03 5.441697e-02 3.071897e-01 2.393279e-01 2.336775e-02
 [163] 7.811297e-02 1.207809e-01 9.132522e-01 2.822558e-01 3.616468e-01 4.337547e-01
 [169] 5.276327e-01 1.372887e-01 1.154214e-01 9.731748e-01 9.534432e-01 3.115336e-01
 [175] 9.112248e-01 8.664478e-01 1.029901e-01 9.599799e-01 8.082578e-01 7.788346e-01
 [181] 4.623716e-01 7.727691e-01 2.929578e-01 7.203916e-01 7.060228e-01 8.977955e-01
 [187] 6.088412e-01 3.310758e-02 1.931544e-01 3.082714e-01 7.191252e-01 3.083520e-01
 [193] 7.085626e-01 8.957885e-01 2.131385e-01 3.813833e-01 6.209185e-01 6.861110e-01
 [199] 8.181485e-01 3.292107e-01 6.250726e-01 4.170549e-01 5.957018e-01 5.708064e-02
 [205] 6.096184e-01 1.855259e-01 6.896119e-01 6.433015e-02 9.616730e-03 8.375038e-02
 [211] 4.780744e-01 7.506058e-01 3.010631e-01 8.907393e-01 5.207517e-01 2.486437e-01
 [217] 5.604200e-01 1.317091e-01 8.677154e-02 3.015195e-01 2.234600e-01 2.048287e-01
 [223] 7.674719e-02 1.650912e-01 6.703660e-01 3.232316e-01 1.945270e-02 8.978293e-01
 [229] 1.084597e-02 4.266679e-01 5.971943e-01 9.793839e-01 7.743327e-01 9.655696e-01
 [235] 1.319445e-02 4.218503e-01 5.937566e-01 4.880964e-01 8.942239e-02 9.063911e-01
 [241] 1.199976e-04 1.047467e-01 4.414818e-01 7.087641e-01 7.376415e-02 8.279286e-01
 [247] 6.347324e-01 4.497344e-01 9.150702e-02 7.478171e-01 7.502178e-01 3.803101e-01
 [253] 4.357242e-01 1.734431e-01 1.241694e-02 3.153093e-01 6.637357e-01 3.759327e-01
 [259] 4.112319e-01 5.811028e-01 7.020535e-01 4.801293e-01 3.805283e-01 4.677460e-01
 [265] 5.298240e-01 6.668545e-01 1.365119e-11 4.217714e-01 7.155633e-01 4.681022e-01
 [271] 9.248369e-01 9.988375e-01 8.621605e-01 5.437873e-01 9.373484e-01 5.626459e-01
 [277] 9.638286e-01 7.623895e-01 4.630994e-01 7.187721e-01 4.676300e-01 9.170011e-01
 [283] 8.058774e-01 9.191612e-01 7.237494e-01 5.722862e-02 4.593763e-01 1.743382e-01
 [289] 8.589921e-02 7.120952e-03 2.680807e-01 6.393466e-10 2.212343e-03 7.452410e-01
 [295] 1.982128e-02 4.895687e-01 5.692250e-01 5.559966e-01 7.779314e-01 4.793040e-01
 [301] 9.266450e-01 9.642336e-01 4.920893e-01 6.403824e-01 9.208956e-01 7.846760e-01
 [307] 2.807756e-01 3.012257e-01 1.375848e-01 4.204743e-02 1.060012e-01 9.214632e-02
 [313] 2.887834e-01 1.549142e-01 7.055205e-01 2.646604e-02 7.212120e-01 2.408660e-02
 [319] 3.616098e-01 8.581399e-01 8.194966e-01 9.714888e-01 9.831340e-01 8.662504e-01
 [325] 5.339039e-01 6.798404e-01 9.038621e-02 7.887190e-01 6.630350e-01 6.291735e-01
 [331] 5.430977e-01 9.791113e-01 6.920811e-01 8.023158e-01 3.232766e-01 2.621769e-01
 [337] 1.093419e-02 2.076407e-03 1.108525e-01 1.566913e-02 2.710024e-01 5.749145e-01
 [343] 9.113369e-01 9.206965e-02 7.542676e-02 6.091652e-01 8.800033e-01 8.162905e-01
 [349] 4.614985e-01 2.393528e-03 4.322948e-01 6.533922e-01 4.171854e-01 4.770953e-04
 [355] 3.212779e-01 8.267626e-02 3.764119e-01 7.388261e-02 3.281737e-01 9.046874e-01
 [361] 2.171592e-01 4.246453e-01 4.353753e-01 9.489382e-01 4.344914e-01 1.375139e-01
 [367] 1.624661e-05 1.003819e-01 9.023715e-01 2.819200e-01 6.615570e-02 9.383740e-01
 [373] 9.437849e-01 7.502559e-01 6.484241e-01 9.596850e-01 3.465740e-01 3.932027e-01
 [379] 1.943787e-01 6.243762e-01 7.596781e-01 8.842522e-01 1.584171e-01 3.066560e-03
 [385] 2.902014e-01 2.728925e-01 8.986439e-03 4.981926e-02 6.714350e-01 8.597792e-01
 [391] 9.227203e-01 3.203839e-01 5.830065e-01 9.601185e-01 6.761848e-01 3.050710e-01
 [397] 6.192624e-01 6.248868e-01 4.813866e-01 3.644365e-01 1.571427e-01 3.701550e-01
 [403] 5.301586e-01 4.432742e-01 9.994002e-02 5.221492e-05 4.810740e-01 2.246047e-02
 [409] 8.347445e-01 4.109802e-01 3.424537e-01 2.218673e-01 6.882193e-01 8.605093e-01
 [415] 1.729863e-02 5.841123e-01 2.953826e-01 4.296254e-03 1.777650e-02 7.582994e-01
 [421] 3.538487e-01 1.618816e-02 5.837873e-02 1.844800e-01 5.358654e-01 4.133316e-01
 [427] 9.983637e-01 4.121201e-01 8.510916e-01 7.080031e-01 3.083660e-02 7.869863e-01
 [433] 1.808036e-01 5.515827e-01 7.556644e-01 5.666515e-01 6.175173e-01 2.989722e-01
 [439] 1.909472e-01 5.680936e-01 4.702056e-01 4.977799e-01 6.887553e-04 6.316879e-01
 [445] 9.994521e-02 6.681028e-01 6.303824e-03 3.139937e-01 2.462693e-01 4.842754e-01
 [451] 7.185334e-01 9.723374e-01 5.279961e-01 5.126336e-01 5.501839e-02 2.385010e-02
 [457] 8.877815e-01 4.268167e-01 6.249365e-01 7.541857e-01 1.688236e-01 4.723426e-01
 [463] 7.960349e-01 2.913975e-01 1.444220e-01 5.358106e-01 5.969194e-02 2.512423e-01
 [469] 4.881835e-01 3.147601e-02 5.181996e-01 1.251334e-02 3.337036e-01 1.662465e-01
 [475] 4.070161e-02 2.284087e-01 7.393071e-01 8.076742e-01 1.425084e-01 7.121986e-01
 [481] 9.028366e-01 9.875243e-01 1.982331e-01 6.640623e-01 1.052511e-01 5.162680e-01
 [487] 4.916457e-02 4.063529e-01 2.687113e-01 1.687104e-01 5.744877e-01 6.972080e-01
 [493] 5.042313e-01 8.714358e-01 5.367125e-01 7.824387e-03 2.443358e-01 5.442655e-01
 [499] 3.039708e-01 3.157710e-02 2.671350e-02 8.133618e-02 4.956365e-01 6.875178e-01
 [505] 2.819803e-02 4.499571e-01 3.225669e-01 1.206915e-01 7.510949e-01 4.686904e-01
 [511] 9.019049e-01 9.321498e-01 5.146484e-01 4.943168e-01 8.524169e-01 4.221998e-01
 [517] 1.162591e-01 9.293396e-01 5.366002e-01 7.544446e-02 3.040374e-01 3.114573e-01
 [523] 1.090054e-01 7.418137e-01 1.871881e-01 8.871482e-03 2.129583e-01 2.391646e-03
 [529] 4.310662e-01 7.975632e-01 2.952263e-01 3.517610e-01 4.537517e-02 9.141052e-03
 [535] 4.203826e-01 3.597611e-01 2.156665e-01 7.971193e-01 3.372495e-01 3.113908e-01
 [541] 4.573662e-01 2.741910e-02 3.663065e-01 1.582637e-01 5.971539e-03 4.034361e-01
 [547] 7.928486e-03 5.566849e-01 4.561209e-01 5.169752e-01 3.264042e-01 3.627485e-02
 [553] 3.949386e-01 3.669585e-01 8.733192e-01 7.346710e-02 2.047444e-01 9.306201e-02
 [559] 3.946652e-01 7.964425e-01 4.469663e-01 9.019875e-01 3.962152e-03 1.621503e-01
 [565] 9.217070e-01 4.003954e-03 7.640834e-01 6.954450e-02 1.755080e-01 8.942880e-01
 [571] 1.792615e-02 1.664076e-01 4.937401e-01 3.133538e-01 6.605614e-01 2.980317e-01
 [577] 3.144637e-01 3.997729e-01 6.706765e-03 5.200022e-01 7.750813e-01 6.253181e-01
 [583] 7.345378e-02 8.382812e-01 4.703207e-01 3.942594e-03 9.244145e-01 7.610229e-01
 [589] 6.767684e-01 6.256713e-01 5.712515e-01 7.953850e-01 2.749314e-01 4.250338e-01
 [595] 7.650676e-01 2.789948e-01 1.287448e-01 2.885587e-01 1.322106e-01 9.571387e-01
 [601] 9.528265e-01 6.466438e-01 9.944465e-01 2.381771e-01 8.802870e-01 9.772657e-01
 [607] 8.972094e-01 1.048052e-01 4.476256e-01 5.929386e-01 5.173140e-01 8.704094e-01
 [613] 1.687019e-01 4.914906e-01 4.420361e-01 5.046681e-01 5.574947e-01 8.110628e-01
 [619] 6.858643e-01 1.190237e-01 4.152257e-01 5.362452e-01 6.465532e-01 7.493356e-01
 [625] 9.684055e-01 8.622536e-01 6.302998e-01 7.845257e-02 2.040127e-02 2.679388e-01
 [631] 5.801192e-01 8.607559e-02 8.869123e-02 8.375751e-01 6.213943e-01 4.668519e-01
 [637] 3.979426e-01 8.514774e-01 2.388480e-01 2.587809e-01 1.398323e-01 3.899975e-01
 [643] 5.275828e-01 5.819829e-01 8.947017e-03 5.825016e-01 8.205341e-01 8.594585e-01
 [649] 4.383700e-01 3.540588e-01 3.999980e-03 8.195700e-02 5.513107e-02 8.804559e-01
 [655] 4.460802e-02 3.610774e-01 9.560762e-01 2.119287e-01 1.100148e-01 5.623630e-01
 [661] 1.541247e-03 4.229121e-01 1.330188e-01 8.216185e-01 2.729476e-01 6.652049e-01
 [667] 8.652747e-01 5.016885e-01 7.600292e-01 6.919445e-01 9.565169e-01 3.066762e-01
 [673] 5.328506e-01 5.318552e-01 1.491969e-01 9.508635e-01 7.185640e-01 6.469556e-01
 [679] 1.384868e-01 5.271885e-01 6.033435e-01 1.933717e-01 4.845026e-01 5.861438e-01
 [685] 9.740393e-01 6.153922e-01 5.912278e-01 8.022706e-01 6.909729e-01 4.882239e-02
 [691] 1.226152e-01 1.896140e-01 2.976366e-03 7.062370e-01 8.247226e-01 3.649172e-01
 [697] 1.756347e-01 6.865414e-01 6.227961e-01 3.062643e-01 3.812476e-02 8.537122e-02
 [703] 9.057651e-01 3.993239e-01 7.227072e-04 2.922580e-01 4.477262e-01 4.324600e-01
 [709] 4.070883e-02 3.960201e-01 1.275019e-01 3.582619e-01 7.378635e-04 3.439765e-01
 [715] 9.332736e-01 9.628696e-01 6.274816e-01 9.717846e-01 4.176568e-04 5.591277e-02
 [721] 6.006515e-01 1.438184e-01 4.783705e-01 7.547687e-01 7.435877e-01 4.962238e-01
 [727] 4.293343e-01 2.258360e-02 3.086084e-01 6.101525e-01 8.760415e-01 7.886039e-01
 [733] 6.799891e-01 2.446572e-01 7.743403e-01 3.164434e-01 9.551240e-01 7.186069e-01
 [739] 6.499554e-01 2.118919e-01 5.113727e-01 4.825803e-01 2.464896e-01 5.680040e-01
 [745] 5.898185e-01 4.185257e-01 9.794990e-02 9.406018e-01 9.687252e-01 4.462771e-01
 [751] 3.800592e-01 7.842664e-01 1.062049e-01 6.597810e-01 2.715464e-01 6.858904e-01
 [757] 9.074613e-01 1.954511e-02 8.951969e-01 4.663258e-01 3.983918e-01 5.161161e-01
 [763] 8.408593e-01 2.901369e-01 7.811782e-01 5.306544e-01 3.213792e-01 3.103223e-01
 [769] 6.238623e-01 3.251497e-01 2.995578e-01 9.611602e-01 6.937948e-01 3.324206e-01
 [775] 7.928994e-01 4.064114e-01 3.131291e-02 5.922049e-01 6.881799e-01 7.761215e-01
 [781] 2.349834e-01 2.215975e-01 3.889381e-01 6.284449e-01 5.596660e-01 9.838749e-01
 [787] 8.635139e-01 9.762162e-01 9.576179e-01 6.910737e-01 9.684219e-01 1.072290e-04
 [793] 8.227578e-01 3.494162e-04 3.599207e-01 5.240435e-01 4.145757e-01 9.604111e-03
 [799] 6.872215e-01 3.109674e-02 4.575313e-01 4.540648e-01 8.626440e-01 1.170019e-01
 [805] 2.478636e-01 7.821157e-01 2.643065e-01 2.225974e-01 8.596173e-02 8.752965e-01
 [811] 9.576422e-01 2.794384e-02 5.700233e-01 9.349886e-01 9.254936e-01 4.100595e-01
 [817] 9.401225e-01 5.997557e-01 2.980548e-01 3.469936e-01 1.641790e-01 6.687500e-01
 [823] 1.377370e-06 1.364465e-01 5.202465e-01 8.946199e-01 2.514533e-01 1.007954e-02
 [829] 2.610219e-02 1.001389e-03 4.790585e-01 1.100708e-01 9.425609e-01 4.353140e-01
 [835] 1.627839e-01 6.095069e-01 3.243661e-01 2.818437e-02 8.555218e-01 4.056604e-01
 [841] 6.050677e-01 6.329992e-01 9.430416e-01 7.529209e-01 2.770969e-01 2.059901e-01
 [847] 2.706809e-01 1.205189e-03 5.987080e-01 8.107302e-01 7.963998e-03 7.753491e-01
 [853] 5.027507e-02 1.550258e-01 8.703869e-02 3.768572e-01 3.299394e-01 1.084049e-01
 [859] 1.367371e-01 8.891284e-01 1.073490e-01 2.090345e-01 3.860939e-01 3.569021e-02
 [865] 9.123115e-01 5.200021e-01 9.628876e-01 7.558977e-01 1.789859e-02 9.013507e-01
 [871] 1.593458e-01 8.622259e-01 5.991593e-03 1.869733e-02 8.489342e-01 5.810539e-01
 [877] 7.386435e-01 9.072509e-01 6.069016e-01 3.981566e-01 6.422527e-01 5.260790e-01
 [883] 3.851367e-01 2.110784e-01 1.215727e-01 9.101522e-01 9.477956e-01 9.769010e-01
 [889] 1.895899e-02 6.548481e-04 4.577718e-01 7.299303e-01 5.909070e-03 1.123766e-01
 [895] 1.876529e-01 2.888938e-01 6.386936e-01 7.309877e-01 9.311258e-01 2.770312e-01
 [901] 9.550502e-01 4.809542e-01 9.486596e-01 1.754825e-01 2.154796e-01 3.618871e-01
 [907] 7.727619e-03 1.539420e-01 1.700089e-01 2.245864e-01 8.669845e-01 1.354407e-01
 [913] 7.958764e-01 8.741414e-01 8.622220e-01 4.649499e-01 2.578447e-01 2.964702e-01
 [919] 4.116013e-01 3.274364e-02 2.409112e-01 1.135170e-01 2.161419e-01 4.196113e-01
 [925] 9.946634e-01 3.705458e-01 1.626570e-02 5.675716e-01 6.590625e-01 4.648076e-03
 [931] 8.017901e-01 1.626664e-01 8.888951e-01 6.788589e-01 5.410045e-01 8.548664e-01
 [937] 9.445873e-01 2.412846e-01 5.212744e-01 7.457951e-01 9.299277e-01 8.027972e-01
 [943] 9.625038e-01 4.555620e-01 9.176344e-01 1.343710e-01 1.726891e-03 6.486595e-01
 [949] 3.847728e-01 1.696002e-01 2.432187e-01 5.855608e-03 2.326925e-01 3.961231e-01
 [955] 4.255313e-02 7.675508e-01 5.448272e-01 3.474887e-01 7.999520e-01 6.672495e-01
 [961] 9.764810e-01 4.959501e-01 2.458427e-02 2.676467e-01 1.715924e-01 3.226133e-01
 [967] 3.091354e-01 3.753057e-01 3.063234e-02 1.024971e-01 5.221563e-02 9.521028e-01
 [973] 2.212798e-01 5.793715e-01 4.244067e-01 8.045358e-01 8.733547e-01 5.956608e-01
 [979] 2.305259e-01 2.423336e-05 9.968167e-01 8.299282e-01 7.409586e-01 9.524011e-01
 [985] 5.593726e-01 5.216346e-01 2.983491e-01 1.282499e-01 1.920044e-02 4.853236e-01
 [991] 3.779592e-01 5.641750e-01 9.515904e-01 7.808364e-01 3.733099e-01 9.352004e-01
 [997] 4.215405e-02 6.725078e-01 6.285389e-01 3.678331e-01
 [ reached getOption("max.print") -- omitted 11897 entries ]

$adjpval
   [1] 3.881910e-01 9.740323e-01 3.080634e-01 7.735434e-01 9.973159e-01 8.876529e-01
   [7] 2.034502e-01 9.740323e-01 8.137830e-01 8.534698e-01 8.933644e-01 9.903281e-01
  [13] 9.901929e-01 7.958258e-01 9.380258e-01 9.241649e-01 7.768961e-01 7.564512e-01
  [19] 3.707840e-01 9.264789e-01 9.927259e-01 9.716760e-01 9.901929e-01 9.790059e-01
  [25] 2.645720e-01 8.916910e-01 6.513966e-01 5.542371e-01 8.862321e-01 8.489865e-01
  [31] 2.778013e-01 9.740323e-01 9.380258e-01 9.917250e-01 9.740323e-01 9.053352e-01
  [37] 4.472130e-01 9.817418e-01 9.886271e-01 3.227749e-01 7.294916e-01 7.801626e-01
  [43] 6.310428e-01 7.761568e-01 3.170895e-01 5.954012e-02 4.765365e-01 9.746053e-01
  [49] 4.073600e-01 7.870689e-01 2.265070e-01 5.746750e-01 7.977963e-01 3.807592e-01
  [55] 9.817628e-01 5.410474e-01 7.940452e-01 9.420696e-01 8.563704e-01 7.970690e-01
  [61] 9.716913e-01 7.829430e-01 8.876148e-01 7.627426e-01 6.811219e-01 3.529406e-01
  [67] 6.779845e-01 7.427190e-01 7.789806e-01 7.926212e-01 3.198416e-01 4.073600e-01
  [73] 6.188562e-01 9.424722e-01 4.551328e-01 8.951609e-01 9.929945e-01 9.705585e-01
  [79] 9.414883e-01 9.740323e-01 9.826470e-01 8.543471e-01 7.427190e-01 8.489865e-01
  [85] 9.700224e-01 9.496695e-01 6.222018e-01 8.890185e-01 8.372785e-01 8.282292e-01
  [91] 9.835841e-01 9.905654e-01 9.740323e-01 8.919908e-01 9.484220e-01 4.093319e-01
  [97] 6.806599e-01 9.716079e-01 9.342185e-01 8.934678e-01 5.172842e-02 9.050572e-01
 [103] 5.662849e-01 9.641410e-01 9.929945e-01 6.829086e-01 3.462012e-01 9.737358e-01
 [109] 8.819970e-01 8.997253e-01 3.306145e-01 4.569189e-01 6.310428e-01 9.886387e-01
 [115] 7.832920e-01 8.047814e-01 6.531155e-01 6.088228e-01 7.293948e-01 9.817418e-01
 [121] 6.189187e-01 3.715780e-01 7.768961e-01 9.683032e-01 9.424722e-01 9.961422e-01
 [127] 2.855872e-03 9.929945e-01 8.781149e-01 7.158195e-01 9.901929e-01 9.682968e-01
 [133] 9.525918e-01 9.790059e-01 9.929945e-01 8.862321e-01 7.283631e-01 7.725756e-01
 [139] 7.866240e-02 1.139090e-01 4.934610e-01 6.513966e-01 9.886387e-01 7.676348e-01
 [145] 1.423324e-01 9.419348e-01 4.647147e-01 9.016588e-01 8.143557e-01 9.112124e-01
 [151] 4.647147e-01 2.884702e-01 5.800885e-01 8.794956e-01 9.741796e-01 2.581130e-01
 [157] 8.319766e-01 1.713949e-01 4.535700e-01 7.860728e-01 7.412281e-01 3.237098e-01
 [163] 5.171759e-01 5.985697e-01 9.886558e-01 7.733700e-01 8.208561e-01 8.622948e-01
 [169] 8.996395e-01 6.224327e-01 5.938730e-01 9.955608e-01 9.929945e-01 7.870689e-01
 [175] 9.886387e-01 9.817418e-01 5.720343e-01 9.934915e-01 9.740323e-01 9.711957e-01
 [181] 8.772002e-01 9.705585e-01 7.789713e-01 9.577250e-01 9.538088e-01 9.864461e-01
 [187] 9.241993e-01 3.728488e-01 6.987101e-01 7.864627e-01 9.572255e-01 7.864627e-01
 [193] 9.543712e-01 9.858353e-01 7.207120e-01 8.352149e-01 9.273869e-01 9.461846e-01
 [199] 9.740323e-01 7.961149e-01 9.290725e-01 8.534698e-01 9.209934e-01 4.611927e-01
 [205] 9.243054e-01 6.885545e-01 9.468673e-01 4.843496e-01 2.161996e-01 5.362353e-01
 [211] 8.819970e-01 9.679595e-01 7.829430e-01 9.853033e-01 8.963212e-01 7.467996e-01
 [217] 9.112124e-01 6.145630e-01 5.426113e-01 7.829430e-01 7.293948e-01 7.124467e-01
 [223] 5.152520e-01 6.624200e-01 9.420696e-01 7.923553e-01 2.972529e-01 9.864461e-01
 [229] 2.281259e-01 8.598716e-01 9.219281e-01 9.956088e-01 9.705585e-01 9.944706e-01
 [235] 2.521019e-01 8.564729e-01 9.206154e-01 8.862321e-01 5.480208e-01 9.886271e-01
 [241] 1.787719e-02 5.751036e-01 8.657462e-01 9.544671e-01 5.103735e-01 9.748664e-01
 [247] 9.320442e-01 8.695671e-01 5.552122e-01 9.674588e-01 9.677018e-01 8.345856e-01
 [253] 8.632395e-01 6.739340e-01 2.448643e-01 7.870689e-01 9.414883e-01 8.317728e-01
 [259] 8.518195e-01 9.178202e-01 9.525918e-01 8.823350e-01 8.349223e-01 8.788927e-01
 [265] 8.997253e-01 9.419348e-01 5.868647e-08 8.564729e-01 9.569287e-01 8.788927e-01
 [271] 9.908317e-01 9.997021e-01 9.817418e-01 9.058674e-01 9.918741e-01 9.120524e-01
 [277] 9.940421e-01 9.686028e-01 8.776898e-01 9.572255e-01 8.788927e-01 9.901929e-01
 [283] 9.740323e-01 9.901929e-01 9.587190e-01 4.618758e-01 8.749927e-01 6.740349e-01
 [289] 5.415967e-01 1.887220e-01 7.639111e-01 1.374275e-06 1.089030e-01 9.663191e-01
 [295] 3.011013e-01 8.866013e-01 9.141196e-01 9.110597e-01 9.709556e-01 8.819970e-01
 [301] 9.909229e-01 9.941432e-01 8.871575e-01 9.336333e-01 9.903281e-01 9.716760e-01
 [307] 7.716004e-01 7.829430e-01 6.226074e-01 4.098103e-01 5.778095e-01 5.561868e-01
 [313] 7.768961e-01 6.504858e-01 9.538088e-01 3.412867e-01 9.582231e-01 3.293551e-01
 [319] 8.208561e-01 9.817418e-01 9.740323e-01 9.955608e-01 9.956088e-01 9.817418e-01
 [325] 9.011593e-01 9.440989e-01 5.516853e-01 9.717364e-01 9.414883e-01 9.296327e-01
 [331] 9.058674e-01 9.956088e-01 9.484220e-01 9.739266e-01 7.923553e-01 7.598418e-01
 [337] 2.285548e-01 1.052838e-01 5.845768e-01 2.694464e-01 7.656822e-01 9.168632e-01
 [343] 9.886387e-01 5.561868e-01 5.129189e-01 9.241993e-01 9.835156e-01 9.740323e-01
 [349] 8.768774e-01 1.139090e-01 8.619097e-01 9.380258e-01 8.534698e-01 4.121597e-02
 [355] 7.913524e-01 5.326052e-01 8.319766e-01 5.104576e-01 7.958258e-01 9.883738e-01
 [361] 7.250069e-01 8.576653e-01 8.632395e-01 9.929945e-01 8.627526e-01 6.225051e-01
 [367] 3.880232e-03 5.675692e-01 9.883738e-01 7.731071e-01 4.883858e-01 9.918741e-01
 [373] 9.927259e-01 9.677018e-01 9.364754e-01 9.934231e-01 8.083064e-01 8.448571e-01
 [379] 6.992375e-01 9.289447e-01 9.685520e-01 9.837797e-01 6.542124e-01 1.292465e-01
 [385] 7.768961e-01 7.672656e-01 2.108809e-01 4.370878e-01 9.422738e-01 9.817418e-01
 [391] 9.905654e-01 7.901321e-01 9.181048e-01 9.935096e-01 9.424722e-01 7.851727e-01
 [397] 9.271564e-01 9.290212e-01 8.835113e-01 8.234285e-01 6.520816e-01 8.269339e-01
 [403] 8.997253e-01 8.663293e-01 5.664046e-01 1.020327e-02 8.833160e-01 3.189505e-01
 [409] 9.751540e-01 8.518195e-01 8.052168e-01 7.281874e-01 9.464666e-01 9.817418e-01
 [415] 2.838427e-01 9.184535e-01 7.801576e-01 1.465841e-01 2.884702e-01 9.683032e-01
 [421] 8.145249e-01 2.747088e-01 4.647147e-01 6.866859e-01 9.025530e-01 8.519638e-01
 [427] 9.995262e-01 8.518195e-01 9.790059e-01 9.543712e-01 3.675597e-01 9.716913e-01
 [433] 6.814244e-01 9.099109e-01 9.682968e-01 9.136427e-01 9.266241e-01 7.812734e-01
 [439] 6.959817e-01 9.136427e-01 8.797282e-01 8.876529e-01 5.286238e-02 9.312794e-01
 [445] 5.664046e-01 9.420696e-01 1.767400e-01 7.870689e-01 7.450211e-01 8.860778e-01
 [451] 9.572255e-01 9.955608e-01 8.996395e-01 8.919908e-01 4.536956e-01 3.275769e-01
 [457] 9.845266e-01 8.599679e-01 9.290212e-01 9.680082e-01 6.682988e-01 8.806842e-01
 [463] 9.736782e-01 7.779246e-01 6.319525e-01 9.025530e-01 4.702212e-01 7.500629e-01
 [469] 8.862321e-01 3.691163e-01 8.952313e-01 2.453077e-01 7.980299e-01 6.644158e-01
 [475] 4.051600e-01 7.308358e-01 9.636021e-01 9.740323e-01 6.289974e-01 9.560453e-01
 [481] 9.883738e-01 9.961422e-01 7.048834e-01 9.414883e-01 5.756847e-01 8.942196e-01
 [487] 4.337041e-01 8.489865e-01 7.639111e-01 6.680559e-01 9.168187e-01 9.500900e-01
 [493] 8.907758e-01 9.817628e-01 9.027872e-01 1.979313e-01 7.427190e-01 9.061592e-01
 [499] 7.848058e-01 3.691163e-01 3.431078e-01 5.277287e-01 8.876529e-01 9.462263e-01
 [505] 3.483429e-01 8.695671e-01 7.919192e-01 5.985697e-01 9.680082e-01 8.793449e-01
 [511] 9.883146e-01 9.912386e-01 8.934678e-01 8.876148e-01 9.794744e-01 8.564729e-01
 [517] 5.938730e-01 9.912386e-01 9.027872e-01 5.129189e-01 7.848058e-01 7.870689e-01
 [523] 5.822956e-01 9.641410e-01 6.921489e-01 2.106664e-01 7.207120e-01 1.139090e-01
 [529] 8.619097e-01 9.737358e-01 7.801576e-01 8.133132e-01 4.217089e-01 2.117538e-01
 [535] 8.559638e-01 8.189831e-01 7.220847e-01 9.737358e-01 8.008666e-01 7.870689e-01
 [541] 8.733824e-01 3.456737e-01 8.241965e-01 6.537884e-01 1.708130e-01 8.483849e-01
 [547] 1.985508e-01 9.110597e-01 8.725291e-01 8.945015e-01 7.948846e-01 3.886897e-01
 [553] 8.457424e-01 8.246496e-01 9.817628e-01 5.090806e-01 7.124467e-01 5.572142e-01
 [559] 8.455986e-01 9.737165e-01 8.687115e-01 9.883146e-01 1.441783e-01 6.570066e-01
 [565] 9.905654e-01 1.441783e-01 9.687831e-01 4.958820e-01 6.744715e-01 9.858353e-01
 [571] 2.888654e-01 6.644453e-01 8.874936e-01 7.870689e-01 9.407311e-01 7.811752e-01
 [577] 7.870689e-01 8.471692e-01 1.824834e-01 8.957667e-01 9.705585e-01 9.292231e-01
 [583] 5.090806e-01 9.756837e-01 8.797282e-01 1.441783e-01 9.908317e-01 9.685520e-01
 [589] 9.426809e-01 9.293261e-01 9.157797e-01 9.735768e-01 7.696961e-01 8.581185e-01
 [595] 9.692836e-01 7.704972e-01 6.113484e-01 7.768961e-01 6.153692e-01 9.929945e-01
 [601] 9.929945e-01 9.355040e-01 9.981614e-01 7.410400e-01 9.835156e-01 9.956088e-01
 [607] 9.863873e-01 5.751800e-01 8.687115e-01 9.203961e-01 8.945380e-01 9.817628e-01
 [613] 6.680559e-01 8.868893e-01 8.657462e-01 8.912371e-01 9.110597e-01 9.740323e-01
 [619] 9.461846e-01 5.967722e-01 8.524788e-01 9.027872e-01 9.355040e-01 9.675373e-01
 [625] 9.948082e-01 9.817418e-01 9.306212e-01 5.185946e-01 3.055673e-01 7.639111e-01
 [631] 9.172242e-01 5.419079e-01 5.464161e-01 9.753887e-01 9.278073e-01 8.788927e-01
 [637] 8.470370e-01 9.790059e-01 7.410400e-01 7.577438e-01 6.254501e-01 8.422301e-01
 [643] 8.996395e-01 9.180327e-01 2.108809e-01 9.181048e-01 9.740323e-01 9.817418e-01
 [649] 8.642717e-01 8.145412e-01 1.441783e-01 5.306222e-01 4.543293e-01 9.835156e-01
 [655] 4.205480e-01 8.205842e-01 9.929945e-01 7.202227e-01 5.841338e-01 9.120524e-01
 [661] 8.718186e-02 8.566152e-01 6.162154e-01 9.740323e-01 7.672656e-01 9.416862e-01
 [667] 9.817418e-01 8.890185e-01 9.685520e-01 9.484220e-01 9.929945e-01 7.855418e-01
 [673] 9.004818e-01 9.002424e-01 6.414552e-01 9.929945e-01 9.572255e-01 9.355040e-01
 [679] 6.231906e-01 8.996395e-01 9.235210e-01 6.987101e-01 8.862048e-01 9.186409e-01
 [685] 9.956088e-01 9.263951e-01 9.203961e-01 9.739266e-01 9.480296e-01 4.320778e-01
 [691] 6.010524e-01 6.951257e-01 1.271066e-01 9.539526e-01 9.741796e-01 8.234285e-01
 [697] 6.747574e-01 9.461846e-01 9.278629e-01 7.853400e-01 3.952533e-01 5.410474e-01
 [703] 9.886271e-01 8.470370e-01 5.356756e-02 7.783716e-01 8.687115e-01 8.619126e-01
 [709] 4.051600e-01 8.462094e-01 6.101865e-01 8.176434e-01 5.406947e-02 8.062784e-01
 [715] 9.912949e-01 9.936090e-01 9.296327e-01 9.955608e-01 3.793324e-02 4.569189e-01
 [721] 9.227387e-01 6.310428e-01 8.819970e-01 9.680082e-01 9.655710e-01 8.876529e-01
 [727] 8.611391e-01 3.197153e-01 7.864627e-01 9.243252e-01 9.826470e-01 9.717364e-01
 [733] 9.440989e-01 7.431206e-01 9.705585e-01 7.870689e-01 9.929945e-01 9.572255e-01
 [739] 9.370702e-01 7.202227e-01 8.919908e-01 8.844448e-01 7.450211e-01 9.136427e-01
 [745] 9.200398e-01 8.539354e-01 5.635917e-01 9.924684e-01 9.948082e-01 8.687115e-01
 [751] 8.345363e-01 9.716760e-01 5.783415e-01 9.403465e-01 7.662653e-01 9.461846e-01
 [757] 9.886271e-01 2.983115e-01 9.858353e-01 8.786273e-01 8.470370e-01 8.942196e-01
 [763] 9.765448e-01 7.768961e-01 9.711957e-01 8.997875e-01 7.913614e-01 7.870689e-01
 [769] 9.284506e-01 7.940666e-01 7.812734e-01 9.935678e-01 9.489601e-01 7.970690e-01
 [775] 9.726751e-01 8.489865e-01 3.687524e-01 9.203961e-01 9.464666e-01 9.705585e-01
 [781] 7.380860e-01 7.281874e-01 8.417599e-01 9.296327e-01 9.112124e-01 9.956088e-01
 [787] 9.817418e-01 9.956088e-01 9.929945e-01 9.480669e-01 9.948082e-01 1.646348e-02
 [793] 9.740323e-01 3.338089e-02 8.189831e-01 8.972378e-01 8.524788e-01 2.161996e-01
 [799] 9.461846e-01 3.686164e-01 8.734134e-01 8.714676e-01 9.817418e-01 5.950211e-01
 [805] 7.463687e-01 9.714683e-01 7.602053e-01 7.283631e-01 5.415967e-01 9.824216e-01
 [811] 9.929945e-01 3.478685e-01 9.147182e-01 9.915118e-01 9.908317e-01 8.516163e-01
 [817] 9.922497e-01 9.223764e-01 7.811752e-01 8.083064e-01 6.608664e-01 9.420696e-01
 [823] 5.383013e-04 6.224327e-01 8.958103e-01 9.858353e-01 7.501720e-01 2.204215e-01
 [829] 3.396972e-01 6.518957e-02 8.819970e-01 5.841670e-01 9.927259e-01 8.632395e-01
 [835] 6.578902e-01 9.243054e-01 7.938396e-01 3.483429e-01 9.806966e-01 8.489865e-01
 [841] 9.235210e-01 9.313175e-01 9.927259e-01 9.680082e-01 7.700164e-01 7.133761e-01
 [847] 7.655639e-01 7.195985e-02 9.222493e-01 9.740323e-01 1.985760e-01 9.705585e-01
 [853] 4.377327e-01 6.504858e-01 5.426113e-01 8.326357e-01 7.962750e-01 5.815412e-01
 [859] 6.224327e-01 9.848202e-01 5.800086e-01 7.168843e-01 8.400804e-01 3.864791e-01
 [865] 9.886387e-01 8.957667e-01 9.936090e-01 9.682968e-01 2.888654e-01 9.882445e-01
 [871] 6.551109e-01 9.817418e-01 1.708130e-01 2.937144e-01 9.790059e-01 9.178202e-01
 [877] 9.632240e-01 9.886271e-01 9.239569e-01 8.470370e-01 9.342582e-01 8.991864e-01
 [883] 8.388968e-01 7.188482e-01 5.996139e-01 9.886387e-01 9.929945e-01 9.956088e-01
 [889] 2.948048e-01 5.172842e-02 8.737433e-01 9.596240e-01 1.708130e-01 5.869601e-01
 [895] 6.926615e-01 7.768961e-01 9.335083e-01 9.599180e-01 9.912386e-01 7.700164e-01
 [901] 9.929945e-01 8.832217e-01 9.929945e-01 6.744715e-01 7.220847e-01 8.209777e-01
 [907] 1.965742e-01 6.494144e-01 6.694977e-01 7.293948e-01 9.817418e-01 6.203047e-01
 [913] 9.736782e-01 9.817628e-01 9.817418e-01 8.781149e-01 7.569788e-01 7.801626e-01
 [919] 8.518195e-01 3.717383e-01 7.417160e-01 5.898430e-01 7.227333e-01 8.549332e-01
 [925] 9.982188e-01 8.272917e-01 2.753112e-01 9.136427e-01 9.402198e-01 1.513608e-01
 [931] 9.739266e-01 6.578902e-01 9.848202e-01 9.438179e-01 9.054179e-01 9.804932e-01
 [937] 9.927739e-01 7.419761e-01 8.963745e-01 9.663939e-01 9.912386e-01 9.739266e-01
 [943] 9.935678e-01 8.721067e-01 9.901929e-01 6.188562e-01 9.290601e-02 9.367105e-01
 [949] 8.387073e-01 6.694423e-01 7.427190e-01 1.705592e-01 7.352845e-01 8.462480e-01
 [955] 4.123273e-01 9.697966e-01 9.064067e-01 8.085083e-01 9.739266e-01 9.419348e-01
 [961] 9.956088e-01 8.876529e-01 3.320035e-01 7.637155e-01 6.723843e-01 7.919192e-01
 [967] 7.866848e-01 8.316849e-01 3.661402e-01 5.705245e-01 4.429301e-01 9.929945e-01
 [973] 7.281874e-01 9.172242e-01 8.576653e-01 9.740323e-01 9.817628e-01 9.209934e-01
 [979] 7.319365e-01 5.452769e-03 9.988402e-01 9.748664e-01 9.641410e-01 9.929945e-01
 [985] 9.112058e-01 8.963745e-01 7.811752e-01 6.103465e-01 2.961495e-01 8.862321e-01
 [991] 8.328330e-01 9.127771e-01 9.929945e-01 9.711957e-01 8.301368e-01 9.915118e-01
 [997] 4.103101e-01 9.424283e-01 9.296327e-01 8.250173e-01
 [ reached getOption("max.print") -- omitted 11897 entries ]
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuaGlzdChpbnZub3JtY29tYiRyYXdwdmFsLCBicmVha3M9MTAwLCBjb2w9XCJncmV5XCIsIG1haW49XCJJbnZlcnNlIG5vcm1hbCBtZXRob2RcIiwgeGxhYiA9IFwiUmF3IHAtdmFsdWVzIChtZXRhLWFuYWx5c2lzKVwiKVxuYGBgIn0= -->

```r
hist(invnormcomb$rawpval, breaks=100, col="grey", main="Inverse normal method", xlab = "Raw p-values (meta-analysis)")
```

<!-- rnb-source-end -->

<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

<img 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/>

<!-- rnb-plot-end -->

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```r

## Summarize the results. DE are the results from the original anlayses of the datasets individually.
HC_Lake_results_metastats <- data.frame(DE, "DE.fishercomb_adjP"=fishcomb$adjpval, "DE.invnorm_adj_P"=invnormcomb$adjpval)
library(data.table)
setDT(HC_Lake_results_metastats, keep.rownames = "Gene")  # make the rownames a part of the dataframe with column name "Gene"
head(HC_Lake_results_metastats)
```

<!-- rnb-source-end -->

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```r
HC_Lake_results <- data.frame(DE, "DE.fishercomb"=ifelse(fishcomb$adjpval<=0.05,1,0), "DE.invnorm"=ifelse(invnormcomb$adjpval<=0.05,1,0))
head(HC_Lake_results)
```

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```r

## Dealing with conflicts in the sign of differential expression 
signsFC <- mapply(HC_Lake_FC, FUN=function(x) sign(x))
sumsigns <- apply(signsFC,1,sum)
commonsngFC <- ifelse(abs(sumsigns)==dim(signsFC)[2], sign(sumsigns), 0)
unionDE <- unique(c(fishcomb$DEindices, invnormcomb$DEindicies))
FC.selecDE <- data.frame(HC_Lake_results[unionDE,], do.call(cbind,HC_Lake_FC) [unionDE,], signFC=commonsngFC[unionDE], DE=DE[unionDE])
  # keep the ones that don't have a conflict (1 or -1)
keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),]
conflictDE <- FC.selecDE[which(FC.selecDE$signFC==0),]
head(FC.selecDE)  # the ones with conflict = 0
```

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```r
head(keepDE)
```

<!-- rnb-source-end -->

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```r
head(conflictDE)
```

<!-- rnb-source-end -->

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<!-- rnb-frame-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxudGFibGUoY29uZmxpY3RERSRERSlcbmBgYCJ9 -->

```r
table(conflictDE$DE)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG4wIDEgXG4yIDEgXG4ifQ== -->

```

0 1 
2 1 
```



<!-- rnb-output-end -->

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```r
### Merge results and write to  a file
setDT(FC.selecDE, keep.rownames = "Gene")
head(FC.selecDE)
```

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```r
head(top.table_HC_Lake)
```

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```r
head(HC_Lake_results_metastats)
```

<!-- rnb-source-end -->

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```r
#Heat_results <- merge(top.table_H_intersect,FC.selecDE, H_results_metastats, by.x = "Gene", all.x = TRUE)
HeatLake_results <- merge(top.table_HC_Lake,FC.selecDE, all.x = TRUE)
HeatLake_results2 <- merge(HeatLake_results, HC_Lake_results_metastats, all.x = TRUE)
head(HeatLake_results2)
```

<!-- rnb-source-end -->

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  </script>
</div>

<!-- rnb-frame-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxud3JpdGUudGFibGUoSGVhdExha2VfcmVzdWx0czIsIGZpbGUgPSBcIk91dHB1dEZpbGVzL01ldGFfSGVhdC1Db250cm9sX0xha2UudHh0XCIsIHJvdy5uYW1lcyA9IEYsIHNlcCA9IFwiXFx0XCIsIHF1b3RlID0gRilcbmBgYCJ9 -->

```r
write.table(HeatLake_results2, file = "OutputFiles/Meta_Heat-Control_Lake.txt", row.names = F, sep = "\t", quote = F)
```

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### Make summary tables
Heat vs Control in Lake ecotype

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyMgTWFrZSBhIHRhYmxlIG9mIHJlc3VsdHNcbmZpc2hjb21iX2RlIDwtIHJvd25hbWVzKGtlZXBERSlbd2hpY2goa2VlcERFWyxcIkRFLmZpc2hlcmNvbWJcIl09PTEpXVxuaW52bm9ybV9kZSA8LSByb3duYW1lcyhrZWVwREUpW3doaWNoKGtlZXBERVssXCJERS5pbnZub3JtXCJdPT0xKV1cbmluZHN0dWR5X2RlIDwtIGxpc3Qocm93bmFtZXMoa2VlcERFKVt3aGljaChrZWVwREVbLFwiREUuMjAwOERhdGFzZXRcIl09PTEpXSxyb3duYW1lcyhrZWVwREUpW3doaWNoKGtlZXBERVssXCJERS4yMDEyRGF0YXNldFwiXT09MSldKVxuSURELklSUihmaXNoY29tYl9kZSxpbmRzdHVkeV9kZSlcbmBgYCJ9 -->

```r
## Make a table of results
fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)]
invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)]
indstudy_de <- list(rownames(keepDE)[which(keepDE[,"DE.2008Dataset"]==1)],rownames(keepDE)[which(keepDE[,"DE.2012Dataset"]==1)])
IDD.IRR(fishcomb_de,indstudy_de)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgIERFICAgIElERCAgIExvc3MgICAgSURSICAgIElSUiBcbjE0OS4wMCAgNjAuMDAgICAwLjAwICA0MC4yNyAgIDAuMDAgXG4ifQ== -->

```
    DE    IDD   Loss    IDR    IRR 
149.00  60.00   0.00  40.27   0.00 
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuSURELklSUihpbnZub3JtX2RlLCBpbmRzdHVkeV9kZSlcbmBgYCJ9 -->

```r
IDD.IRR(invnorm_de, indstudy_de)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiICAgIERFICAgIElERCAgIExvc3MgICAgSURSICAgIElSUiBcbjEzNi4wMCAgNjAuMDAgIDEzLjAwICA0NC4xMiAgMTQuNjEgXG4ifQ== -->

```
    DE    IDD   Loss    IDR    IRR 
136.00  60.00  13.00  44.12  14.61 
```



<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


### Make Upset Plot, Treatment in Fast-living

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBIZWF0IENvbnRyb2wgaW4gTGFrZSAgMjAwOFxuICAjU3Vic2V0IHRoZSB0b3AudGFibGVzIHRvIG9ubHkgY29udGFpbiB0aGUgcm93cyB3aGVyZSBcImFkai5QLlZhbCA8PTAuMDVcblVQX0hDX0xha2UwOF90b3AgPC0gdG9wLnRhYmxlX0hDX0xha2UwOFt3aGljaCh0b3AudGFibGVfSENfTGFrZTA4JGFkai5QLlZhbDw9MC4wNSksIF1cbmRpbShVUF9IQ19MYWtlMDhfdG9wKVxuYGBgIn0= -->

```r
# Heat Control in Lake  2008
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_HC_Lake08_top <- top.table_HC_Lake08[which(top.table_HC_Lake08$adj.P.Val<=0.05), ]
dim(UP_HC_Lake08_top)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDIyICA3XG4ifQ== -->

```
[1] 22  7
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuICAjIGNvbnZlcnRpbmcgY29sdW1uIG9mIFwiR2VuZVwiIGluIGRhdGFmcmFtZSBjb2x1bW4gaW50byB2ZWN0b3IgYnkgcGFzc2luZyBhcyBpbmRleFxuSENfTGFrZV9IUzIwMDggPSBVUF9IQ19MYWtlMDhfdG9wW1snR2VuZSddXVxuIyBIZWF0IENvbnRyb2wgaW4gTGFrZSAyMDEyXG4gICNTdWJzZXQgdGhlIHRvcC50YWJsZXMgdG8gb25seSBjb250YWluIHRoZSByb3dzIHdoZXJlIFwiYWRqLlAuVmFsIDw9MC4wNVxuVVBfSENfTGFrZTEyX3RvcCA8LSB0b3AudGFibGVfSENfTGFrZTEyW3doaWNoKHRvcC50YWJsZV9IQ19MYWtlMTIkYWRqLlAuVmFsPD0wLjA1KSwgXVxuZGltKFVQX0hDX0xha2UxMl90b3ApXG5gYGAifQ== -->

```r
  # converting column of "Gene" in dataframe column into vector by passing as index
HC_Lake_HS2008 = UP_HC_Lake08_top[['Gene']]
# Heat Control in Lake 2012
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_HC_Lake12_top <- top.table_HC_Lake12[which(top.table_HC_Lake12$adj.P.Val<=0.05), ]
dim(UP_HC_Lake12_top)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDgzICA3XG4ifQ== -->

```
[1] 83  7
```



<!-- rnb-output-end -->

<!-- rnb-source-begin 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 -->

```r
  #converting column of "Gene" in dataframe column into vector by passing as index
HC_Lake_HS2012 = UP_HC_Lake12_top[['Gene']]
# Heat MetaAnalysis
## Keep the genes from the meta-analysis that have invernorm adjusted p-value <= 0.05 and the log fold change is going in the same direction in both analyses
UP_MetaA <- HeatLake_results2[which(HeatLake_results2$DE.invnorm_adj_P<=0.05), ]
UP_MetaB <- UP_MetaA[which(abs(UP_MetaA$signFC)==1),]
dim(UP_MetaB)
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDEzNiAgMjNcbiJ9 -->

```
[1] 136  23
```



<!-- rnb-output-end -->

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuICAjY29udmVydGluZyBjb2x1bW4gb2YgXCJHZW5lXCIgaW4gZGF0YWZyYW1lIGNvbHVtbiBpbnRvIHZlY3RvciBieSBwYXNzaW5nIGFzIGluZGV4XG5IQ19MYWtlX01ldGEgPSBVUF9NZXRhQltbJ0dlbmUnXV1cblxubGlzdEhDX0xha2UgPC0gbGlzdChIUzIwMDggPSBIQ19MYWtlX0hTMjAwOCwgSFMyMDEyID0gSENfTGFrZV9IUzIwMTIsIE1ldGFBbmFseXNpcyA9IEhDX0xha2VfTWV0YSlcblxuIyBNYWtlIHVwc2V0IHB1dCBhbmQgc2F2ZSBhcyBhIC5wZGYgZmlsZVxucGRmKFwiT3V0cHV0R3JhcGhzL1Vwc2V0X0hlYXRDb250cm9sX0xha2UucGRmXCIpXG4gICNtYWtlIHBsb3RcbnVwc2V0KGZyb21MaXN0KGxpc3RIQ19MYWtlKSwgbWFpbmJhci55LmxhYmVsID0gXCJOdW1iZXIgb2YgR2VuZXMgaW4gSW50ZXJzZWN0aW9uXCIsIHNldHMueC5sYWJlbCA9IFwiRGlmZmVyZW50aWFsbHkgZXhwcmVzc2VkIGdlbmVzIChhZGp1c3RlZCBwLXZhbHVlPTAuMDUpXCIsb3JkZXIuYnkgPSBcImZyZXFcIilcbmRldi5vZmYoKVxuYGBgIn0= -->

```r
  #converting column of "Gene" in dataframe column into vector by passing as index
HC_Lake_Meta = UP_MetaB[['Gene']]

listHC_Lake <- list(HS2008 = HC_Lake_HS2008, HS2012 = HC_Lake_HS2012, MetaAnalysis = HC_Lake_Meta)

# Make upset put and save as a .pdf file
pdf("OutputGraphs/Upset_HeatControl_Lake.pdf")
  #make plot
upset(fromList(listHC_Lake), mainbar.y.label = "Number of Genes in Intersection", sets.x.label = "Differentially expressed genes (adjusted p-value=0.05)",order.by = "freq")
dev.off()
```

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoibnVsbCBkZXZpY2UgXG4gICAgICAgICAgMSBcbiJ9 -->

```
null device 
          1 
```



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<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

Heat vs Control in Meadow (slow-living) ecotype MetaAnalysis

first get the data ready

#Merge top tables results for each dataset - keep the genes in common ~13000
library(plyr)
top.table_HC_Meadow <- merge(top.table_HC_Med08,top.table_HC_Med12,by="Gene",all.x = FALSE, suffixes = c(".08", ".12"))
dim(top.table_HC_Meadow)

[1] 12897    13

summary(top.table_HC_Meadow)

     Gene              logFC.08          AveExpr.08          t.08            P.Value.08       
 Length:12897       Min.   :-5.89927   Min.   :-2.530   Min.   :-4.94773   Min.   :0.0000008  
 Class :character   1st Qu.:-0.43958   1st Qu.: 1.378   1st Qu.:-0.74043   1st Qu.:0.1789296  
 Mode  :character   Median : 0.02288   Median : 3.343   Median : 0.04061   Median :0.4279362  
                    Mean   : 0.14379   Mean   : 3.261   Mean   : 0.11051   Mean   :0.4476621  
                    3rd Qu.: 0.65689   3rd Qu.: 4.970   3rd Qu.: 0.91075   3rd Qu.:0.6964138  
                    Max.   : 8.69897   Max.   :15.915   Max.   : 8.62925   Max.   :0.9999825  
  adj.P.Val.08          B.08           logFC.12           AveExpr.12          t.12          
 Min.   :0.01001   Min.   :-6.685   Min.   :-6.338470   Min.   :-5.122   Min.   :-7.444406  
 1st Qu.:0.71782   1st Qu.:-6.074   1st Qu.:-0.222439   1st Qu.: 1.965   1st Qu.:-0.932301  
 Median :0.85741   Median :-5.459   Median : 0.000503   Median : 3.609   Median : 0.001512  
 Mean   :0.82477   Mean   :-5.369   Mean   : 0.028889   Mean   : 3.543   Mean   : 0.069307  
 3rd Qu.:0.92885   3rd Qu.:-4.972   3rd Qu.: 0.259839   3rd Qu.: 5.057   3rd Qu.: 0.966428  
 Max.   :0.99998   Max.   : 6.148   Max.   : 8.168621   Max.   :16.730   Max.   : 8.859638  
   P.Value.12      adj.P.Val.12            B.12       
 Min.   :0.0000   Min.   :0.0004805   Min.   :-6.417  
 1st Qu.:0.1050   1st Qu.:0.4270778   1st Qu.:-6.109  
 Median :0.3554   Median :0.7106967   Median :-5.633  
 Mean   :0.4006   Mean   :0.6396243   Mean   :-5.128  
 3rd Qu.:0.6675   3rd Qu.:0.8881298   3rd Qu.:-4.759  
 Max.   :0.9999   Max.   :0.9999412   Max.   : 8.954  

dim(top.table_HC_Meadow)

[1] 12897    13

head(top.table_HC_Meadow)

#Put p-value and Fold changes results in lists
HC_Meadow_rawpval <- list("pvalue_08"=top.table_HC_Meadow[["P.Value.08"]], "pvalue_12"=top.table_HC_Meadow[["P.Value.12"]])
summary(HC_Meadow_rawpval)

          Length Class  Mode   
pvalue_08 12897  -none- numeric
pvalue_12 12897  -none- numeric

HC_Meadow_adjpval <- list("adjpvalue_08"=top.table_HC_Meadow[["adj.P.Val.08"]], "adjpvalue_12"=top.table_HC_Meadow[["adj.P.Val.12"]])
summary(HC_Meadow_adjpval)

             Length Class  Mode   
adjpvalue_08 12897  -none- numeric
adjpvalue_12 12897  -none- numeric

HC_Meadow_FC <- list("HM_FC_08"=top.table_HC_Meadow[["logFC.08"]], "HC_Meadow_FC_12"=top.table_HC_Meadow[["logFC.12"]])
summary(HC_Meadow_FC)

                Length Class  Mode   
HM_FC_08        12897  -none- numeric
HC_Meadow_FC_12 12897  -none- numeric

#make matrix of 1 for genes DE and 0 for not.
DE<- mapply(HC_Meadow_adjpval, FUN=function(x) ifelse(x <= 0.05, 1, 0))
studies <-c("2008Dataset", "2012Dataset")
colnames(DE)=paste("DE", studies,sep=".")
rownames(DE)=paste(top.table_HC_Meadow$"Gene")
#DE<-cbind(top.table_HC-Lake$"Gene", DE)
summary(DE)

 DE.2008Dataset      DE.2012Dataset   
 Min.   :0.0000000   Min.   :0.00000  
 1st Qu.:0.0000000   1st Qu.:0.00000  
 Median :0.0000000   Median :0.00000  
 Mean   :0.0005428   Mean   :0.02985  
 3rd Qu.:0.0000000   3rd Qu.:0.00000  
 Max.   :1.0000000   Max.   :1.00000  

head(DE)

      DE.2008Dataset DE.2012Dataset
A1CF               0              0
AAAS               0              0
AACS               0              0
AADAT              0              0
AAGAB              0              0
AAK1               0              0

dim(DE)

[1] 12897     2

#Check for normal distributions
par(mfrow = c(1,2))
hist(HC_Meadow_rawpval[[1]], breaks=100, col="grey", main="2008 Dataset", xlab="Raw p-values")
hist(HC_Meadow_rawpval[[2]], breaks=100, col="grey", main="2012 Dataset", xlab="Raw p-values")

Calculate the meta-analysis p-value

Heat vs Control in Meadow (slow-living) ecotype

library(metaRNASeq)
# P-value combination using Fisher method 
fishcomb <- fishercomb(HC_Meadow_rawpval, BHth = 0.05)
summary(fishcomb)

              Length Class  Mode   
DEindices       384  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric

hist(fishcomb$rawpval, breaks=100, col="grey", main="Fisher Method", xlab = "Raw p-values (meta-analysis)")

# P-value combination using inverse nomral method method 
invnormcomb <- invnorm(HC_Meadow_rawpval, nrep=c(12,20), BHth=0.05)
summary(invnormcomb)

              Length Class  Mode   
DEindices       440  -none- numeric
TestStatistic 12897  -none- numeric
rawpval       12897  -none- numeric
adjpval       12897  -none- numeric

head(invnormcomb)

$DEindices
  [1]    26    96   127   138   139   163   219   229   267   292   350   367   405   406   419
 [16]   455   534   552   559   563   566   629   670   705   708   709   792   823   828   840
 [31]   848   895   930   963   980  1002  1071  1137  1213  1248  1255  1260  1285  1286  1334
 [46]  1384  1385  1443  1488  1566  1637  1685  1689  1756  1804  1842  1844  1848  1880  1884
 [61]  1889  1895  1900  1931  2030  2058  2059  2092  2096  2133  2140  2196  2215  2224  2226
 [76]  2227  2231  2242  2283  2314  2368  2371  2449  2458  2477  2534  2595  2608  2611  2612
 [91]  2621  2650  2654  2690  2766  2772  2777  2841  2889  2913  2920  2979  3055  3059  3099
[106]  3115  3119  3144  3150  3169  3201  3209  3236  3252  3264  3277  3328  3365  3385  3428
[121]  3513  3514  3559  3593  3595  3619  3670  3673  3697  3704  3708  3712  3716  3718  3723
[136]  3813  3866  3932  3975  3981  3983  3985  3998  4006  4007  4034  4058  4070  4085  4097
[151]  4105  4116  4129  4131  4139  4156  4163  4177  4192  4198  4199  4232  4269  4286  4334
[166]  4405  4501  4512  4515  4517  4593  4600  4623  4673  4728  4776  4828  4829  4835  4839
[181]  4894  4933  4962  4994  5013  5200  5236  5240  5253  5263  5273  5310  5322  5350  5353
[196]  5378  5414  5543  5638  5738  5752  5760  5763  5800  5841  5848  5977  6072  6185  6234
[211]  6267  6290  6331  6342  6369  6410  6492  6695  6707  6708  6751  6787  6788  6801  6892
[226]  6919  6964  7003  7050  7152  7212  7285  7305  7340  7378  7488  7495  7506  7508  7571
[241]  7589  7609  7615  7616  7623  7625  7626  7627  7670  7681  7729  7761  7795  7824  7826
[256]  7836  7890  7905  7917  7970  8001  8171  8275  8341  8444  8450  8452  8461  8472  8491
[271]  8496  8517  8534  8561  8606  8636  8647  8657  8692  8701  8723  8730  8747  8756  8767
[286]  8781  8789  8814  8832  8851  8869  8875  8909  8958  9007  9013  9067  9086  9101  9108
[301]  9111  9117  9135  9158  9173  9184  9220  9243  9256  9270  9298  9376  9386  9390  9412
[316]  9425  9435  9536  9537  9558  9565  9699  9798  9811  9839  9892  9922  9923  9941  9951
[331] 10001 10017 10069 10070 10092 10093 10095 10134 10163 10299 10316 10317 10324 10329 10343
[346] 10355 10356 10369 10374 10383 10404 10410 10458 10500 10519 10562 10564 10573 10576 10591
[361] 10604 10656 10673 10677 10686 10692 10763 10879 10889 10893 10936 10964 11093 11094 11107
[376] 11138 11178 11218 11228 11275 11312 11340 11381 11393 11398 11435 11463 11476 11563 11572
[391] 11586 11588 11616 11636 11743 11745 11812 11929 11931 11936 11937 11938 12043 12048 12080
[406] 12084 12094 12124 12127 12135 12140 12177 12204 12329 12340 12363 12374 12375 12439 12485
[421] 12613 12653 12668 12671 12674 12675 12684 12698 12701 12719 12723 12761 12785 12787 12835
[436] 12852 12883 12886 12887 12889

$TestStatistic
   [1]  0.7397157533  2.1865558709  2.8176350245  0.7077621513 -0.4685765258 -0.1877302227
   [7]  1.5531475570 -1.2898429399  1.4906507692 -0.0313097093 -0.0947709010  2.1998513051
  [13] -0.3675166439 -0.9513320171  0.0851390322 -0.3457188394  1.2965240320  0.3387089530
  [19]  1.4837400485  0.6394470433  0.1682091630 -0.2786266013 -1.3340420510 -1.6540209613
  [25]  0.6102739284  3.0059326549  0.8254496192  1.9149009660  1.9349494076 -0.8229875142
  [31]  0.8528282120  1.1279645538  0.9963898370  2.8444245979 -0.5321678356  0.0529952228
  [37]  1.7098966178 -0.5263166345  0.6551320501  2.4339207428  0.1505513153  0.9235852513
  [43] -1.4154597356  0.5218069971 -1.2920266022  2.5966518538  1.9266719497 -0.5483878489
  [49]  0.8802421480  1.0535710132  1.2144889963 -0.8664332053 -3.5515111094  2.2362627233
  [55]  0.0050629508  0.9976794719  0.1301276252  2.4355477003  1.4333162076 -1.2245373475
  [61]  0.5957327712 -0.9944743614 -1.2523023314  0.9563918621 -0.7849209234  1.4425945388
  [67]  0.4357722133  2.3440469067 -0.8064439791 -0.3862835792  0.3252674998  0.2999144287
  [73]  1.5395910205 -1.6407254320  1.4343183726  1.7043320981 -0.1657392312  2.3125211185
  [79] -0.2623967751  1.3548697961 -0.6411776693  0.0412695432  1.1400450933  0.4415791251
  [85]  0.5594581358  0.5532068086  0.8462503029  0.0947763800  0.8482617036  0.6616806520
  [91] -2.5209229978  1.4791969366  0.6090870140 -0.0699045741 -1.0702798304  3.0267752153
  [97]  0.5254337132 -0.0639556776 -0.3666857652  0.5359714668  0.7494696983  0.6349035602
 [103]  0.7235054066 -1.3344213719  0.8983108494  0.4636856498 -0.0774550012 -0.1547959299
 [109] -1.9342343889 -1.1293777673 -1.1239056313  0.4597623576  0.0732350794  0.0864404138
 [115] -1.6567650786  0.4941222868 -1.1202223623  2.1989602853 -0.0611392005 -0.2494493184
 [121]  0.9025916594  0.9957961874 -1.0428555610  0.4211217545 -1.6563042726  0.4435914772
 [127]  3.6339262547  0.6478377769 -0.2393199876 -0.0629775236 -0.0395207485 -0.5681827298
 [133] -1.6259987122  0.9572986371 -0.9906068264  0.1249923044 -0.0703632901  3.8690667799
 [139]  3.9852990287  2.2146314110  0.2831517189  0.0309522807  0.3731261610 -1.9522617283
 [145]  1.9171065479  2.5702464428  1.6241427624  0.2375403433  0.6246751316  1.9897321749
 [151] -0.2085227778  0.4379240086 -1.3425068501  0.0462342681 -0.4333527894 -0.5681800176
 [157]  1.7281839499  1.7894475407  0.4748354847 -0.1341904718  1.4553100863  2.1398234569
 [163]  3.3226515185  2.0735339157  0.8636786722 -0.2305929709  0.6989567013 -0.0913035452
 [169]  1.2055019164  0.6102313480  0.5744874906 -0.6545958298 -1.9848948070  0.7762918714
 [175]  1.3877652513 -1.2676578293  2.5533434218  1.0921738523  1.1000495870  0.4025668165
 [181]  0.3531695848  0.7532792456 -1.3884864812  0.0230088629  1.6035590309  2.4061908555
 [187]  0.2632191627 -0.9086518552  0.4764558164  1.2263303023 -0.3999667431  0.0197141689
 [193] -1.9593762857 -1.2668261425 -1.7146418693 -2.3468411707 -0.7557553514  1.8297875243
 [199]  0.4024786690  0.7069884611  0.1766668115  0.8168069006  0.4562347938  1.6623009365
 [205]  0.9470786500  2.0273363564  0.0454813073  1.9849288771  2.2168663321 -0.8173440088
 [211]  0.1624035535 -0.4212082480  1.2988309762  0.5318245921 -2.1949440816  1.2844029241
 [217] -0.4399465634 -0.4966463878  3.6589940688  0.7706201289  1.3751057180  1.0177168791
 [223]  0.8775357365  0.5203822081 -1.0711515911  0.6309929638  1.0992937413  0.1850275887
 [229]  3.0832933444  1.8212667851 -1.6362284334 -0.3344188978  0.9799395552  0.5535605186
 [235]  1.2945265797  1.0030904277  0.0464980726  0.9486421355  1.7705889793  0.3463035509
 [241]  2.6964284851  0.0416280859  0.4982750655 -1.8850312085 -0.5876126818 -0.0581512261
 [247]  0.6376849898  1.9909832047 -0.9882734872  2.4847805958  0.4193351056  0.9865092575
 [253]  0.5807639552  1.5334984594  2.1393511870  1.5343924688  1.1939098778  0.6543463388
 [259] -0.4157187985  0.6232526433 -0.1381722736  1.7026494758 -0.5675927117 -0.2664325079
 [265]  0.6116620532 -0.5108946083  5.6768253432  0.5147908671  1.1426252625  0.3220293651
 [271] -0.2961592215 -0.0887451829 -1.7623855016 -0.1528282645 -0.3374484549 -0.0653016683
 [277] -0.2737539125  0.5931791938  0.8461241918 -0.0081041293  0.7276986199 -0.0612827269
 [283]  1.3181186281 -0.5019669154 -0.7327643879  2.5578850755 -0.2882422973 -0.0143988970
 [289]  2.4532733202  0.2369890876  0.2679468484  5.9717056246  0.9474695593  1.1900472552
 [295]  2.9007472937 -0.5908575523 -0.1058568739 -0.1142765596 -0.2446024910 -0.2136784654
 [301] -2.5971430295 -0.5126128630  0.5368103149 -1.4195844809  1.1500701937 -0.2412366308
 [307] -0.1486329214 -2.0640708742  0.1520443674 -1.3055223505  0.0285245770 -1.0606535133
 [313] -1.4204685770  0.0604981025 -0.7884176207 -1.4220458053 -2.9165395386 -0.6823890578
 [319]  0.3091204995  1.0306209338 -1.2402341022 -0.8126351773 -0.0918829621  1.2559523145
 [325] -0.9287206745  1.2078454942  0.1088869810  0.1758601599  0.6464716534  1.5034714320
 [331] -0.4554752730 -2.0379210206 -0.1592424936  0.5817270544 -0.4754303773 -0.0365978354
 [337]  0.2732435032  0.8435819365  1.0999624588 -0.4017880234  2.2849107904 -0.9574438086
 [343] -0.1711296117  1.5992016163 -0.1265899964 -0.6212358341  1.5863122081  1.5315291237
 [349]  0.7711935391  4.6106498471  0.3985496191  0.6079670104 -0.3744297720  0.8445222933
 [355]  0.2807791861 -0.9566284953  1.9516965496  2.7448816989  0.4010115108 -0.8680932800
 [361]  0.3142142878  1.8779271530  0.3984472189  1.9511233782  2.5624120096  0.0566113169
 [367]  4.7415419839 -0.2154360298  2.1349105994 -0.5956282813  0.4915367443 -0.0009930529
 [373]  1.3301928571  2.5163037709  0.9439927925  0.3270547758  0.0742266025  1.1176264729
 [379]  0.7453374451 -1.1998629421  1.3452231651 -0.4187163106 -1.0535672455  2.4786306044
 [385]  0.2832601667 -0.1287608423  0.9092592467  2.0069661866  1.6519985787  1.3188202568
 [391]  1.5903041057  0.2992303579 -0.6633822021  0.3293880662  0.4031933957  0.1140431546
 [397] -0.4813210580  1.7500205364  0.0630351588 -0.7057302514  1.7955465851 -1.4196302140
 [403] -0.1039938814  1.6565550520  3.5298781732  3.6880556428  0.0921098062  2.4184483187
 [409] -0.7820179018 -1.2615202269 -2.6500286132  0.1530656665 -0.2085959198  0.8855932500
 [415]  0.0920480853  0.2576928090  0.9306446126  2.4360064756  3.6543518603  0.4318133946
 [421]  0.9175148016  2.6537454826  1.7695674854 -0.4452157340 -0.3745996932  2.5335832213
 [427]  1.5081436556  0.4808708637  1.3272515909 -0.0650546694  1.3676410480  0.2971847385
 [433]  0.3641072898  0.6070818476  0.9119903030  1.4744913754 -1.1075078686  1.3047733228
 [439]  1.3196159712  1.6425250372  0.8148322304  0.7712178746  1.6023660360 -1.4146475177
 [445]  0.9561180019 -0.3420570651 -0.5063447354 -0.6178974829  1.0841309514 -1.0289443051
 [451]  0.8877496137  0.5906629998 -0.1837352035 -0.7992313301  3.6691399016  0.5609917063
 [457] -0.0540718926  1.0926644773  0.2917496776  0.6121568625 -1.5171759394 -0.4432812967
 [463] -0.7719738717  1.3308661631 -1.3233160833  2.6544806734  0.7363652738 -0.5584434522
 [469]  2.5217872605  0.5118344247  2.0600547695  1.9181859586  0.4125756802  2.0444667000
 [475]  2.7744906694  0.2701685523 -1.0105260692  1.1181362961  2.0776758191 -0.5757673411
 [481] -0.1373051905 -0.5876645998  0.7066324039 -1.1352260712  2.3277636849  0.8239711036
 [487]  1.5059258353 -1.2435830597  1.4987865593 -0.3462233325 -0.8625467441  0.4359110432
 [493]  0.9169417649 -0.6253966215 -0.0845247533  2.3884525426  0.5226858258  1.0946185963
 [499]  1.2530227497  1.7628484491 -0.7821564014 -0.5110451974 -1.8401347810 -1.1004426905
 [505]  0.4585375124  2.0739081598 -3.1916650388  0.4643897202  0.5688486599  1.2477258892
 [511]  0.2008046116  0.1270749106 -0.3505894076 -0.4491492575  0.3660516959 -0.4715597398
 [517] -1.1987213551 -1.0561565780  0.7341299166 -2.0666637030  1.6605649515 -0.6795099003
 [523]  0.5635570236 -0.7175632444 -1.9200990874  2.6054664448  0.4433866498  1.2628239057
 [529] -0.0141781199  1.4108820965  0.7441343374 -1.9478363322  1.9486793702  3.3981384090
 [535] -0.8099834406 -0.2202710240  0.7806587458  0.0931913537  1.7293932863 -1.8825709072
 [541]  2.0746982387  1.9196402519 -0.7890532329  2.3038936162  1.8760385838  0.6286411457
 [547]  2.8517420331  1.8188384582  2.0384358455  1.1571355408 -0.9879164056  3.5134017151
 [553]  0.5324206213 -0.5744239011  0.6988490602 -0.0681810419 -0.2562527157  0.7646467981
 [559]  2.9836849213  0.6654867600 -1.5563650422  2.1948851538  3.2801091590  0.7840315673
 [565]  0.1559660453  4.1690139913  1.2131142667  1.5735783189  1.8191760471  1.0078698751
 [571]  2.5444002804  0.4801293930  0.3614711768  2.7731245361  1.3943512315  2.0766843946
 [577]  2.4426494988  0.8213617492 -0.8019987773  0.1569267624  1.9865793878  1.9329512363
 [583] -1.1509738903  0.9757209477 -0.5137477183  0.8035396765  0.3176485813  1.4737418123
 [589]  0.2871967313  1.0340508908  0.5495569732 -0.0223874454  1.2370961704 -0.4038113267
 [595]  1.8029530027  0.1029269606  1.1047724437  0.7721947473  1.1310458748  1.0154695427
 [601] -0.4782525698  0.3835285361 -0.2311519042  1.0679207558 -0.0821217428  0.7256753310
 [607]  2.8509808798 -1.5275251193  1.6302677844 -0.4664828828  1.2673570925  1.5209697585
 [613]  0.4075280566  0.0304745417  0.5861876442  1.2908972318  0.7507235596  0.0646041029
 [619]  0.6637761641  0.1108644934 -2.1744393963  0.9025721860  0.4692174757 -0.4444594129
 [625]  0.8804215507 -0.0074765091  0.3108738069 -0.5284308357  3.1654999588  1.3214774165
 [631] -1.6963234808 -0.3481156084 -0.3577897569  0.7838286324 -1.5426552420  1.3195596890
 [637]  0.9592868893  1.3206400915  0.6423020605  1.6906556513 -1.3961341996  0.1553905956
 [643]  0.6958888701  1.0184140938  2.9210978382  0.1367401362  1.2355697958 -0.5484968204
 [649] -1.8781871489  0.9300842145  1.9557745195  2.2376851929  2.2526372241 -0.2538244871
 [655] -1.1682932242  1.3904894049 -0.3591676743  0.3232781067  1.5358342274 -2.1890810189
 [661]  0.4506865129  1.2598551769 -0.2389380884 -0.4701238416 -0.0837487892 -0.0516231711
 [667] -0.4238169762  0.5607653479  1.5737698959  3.7739827478  0.2104950941  1.6890418765
 [673] -1.1632690194  0.0156648636  0.6511153573  0.1985221276 -0.2410934771 -1.2266272322
 [679]  1.2117399557  1.7043103707  1.5900701053 -0.0982877314 -0.6126739175  2.4499856304
 [685] -0.5083178591 -0.9937998706 -0.1857595778 -1.0245544166  1.5318091648  0.5349519870
 [691] -0.7991138237  0.3971540306  1.3639129954  1.3348016009  0.1745494078  0.3021335012
 [697]  0.0310566715 -1.4119105952  0.7196801759 -1.5878114498  1.8536956849  1.3741888452
 [703]  0.8165464024  1.1209774640  3.0204947833  0.0113151668  0.5186365681  2.9323288695
 [709]  4.0653122776  1.9661749864 -0.1182942599  2.0585211799 -0.6102361888  0.8098123364
 [715]  2.7531404668 -1.7939339349  0.6001726085 -0.9518035131  0.7614287916  0.2846136366
 [721] -0.6195554023  2.0155195903  0.1845953412  0.8542947832  2.0158082859  0.0218064450
 [727]  0.1025935985 -1.9957241117 -0.0233644008 -1.0186088278 -0.2763847001 -0.2941592006
 [733] -0.2629043568  1.2282717754  1.4439075058 -0.9835559162 -0.2407473755  0.8260256066
 [739]  0.2308496619 -0.4350562493  0.3271666987 -1.8660109225  0.5524785785  0.0248334750
 [745]  1.5896443505  1.8027196375  0.7158000773  1.3194907742 -2.2906597423 -1.1633798799
 [751]  0.6518095212 -0.0766332758  0.2190880456  0.9192085421 -0.7777972263 -0.3054758870
 [757]  1.2070506043  0.2103474410 -0.5057346010  0.0229457633  0.7110275715  1.6038554871
 [763] -1.6219118919  1.4677938803  0.9223537624 -1.2414761751 -1.8664035003  1.5562162206
 [769] -0.4195232671 -0.4815701002 -0.3695790626 -0.5215128482 -1.0039842577  1.1050025302
 [775] -2.0748036718  0.8457919124  2.5527760799 -1.7186262536  0.9086027320  0.5357768206
 [781]  1.1118197647 -0.1612662612  1.9498432130 -0.3983448152  0.2701556119 -0.7960047805
 [787]  1.7582025964  0.3860165571 -0.8713589740  0.5316386912  0.9786349303  4.2816324370
 [793] -1.7002772058  2.9240544724 -0.4256275059 -0.8488796374 -2.0923638708 -0.4760656814
 [799]  0.7083131027  1.1843140219  0.6528352563 -0.0817909848  0.0080112170  1.9950258289
 [805] -1.5147649740  2.1330896753  0.1240856484 -1.6685837830  1.6390697275 -1.4924108247
 [811] -0.6332779781  1.5994794697  1.1366048270 -0.0301908798 -0.8202423492  0.0240264967
 [817]  0.8228230457  0.7407583452  1.1807435361 -0.1736294933 -0.7503961541 -0.8894200483
 [823]  4.9365457015  1.7620926111 -0.8378966776 -1.3925360643  1.7071208269  3.8095606059
 [829]  0.7849502689  1.5788289742 -0.2702546578  1.5553211966  1.3021530672  1.0731491114
 [835] -0.5997921545  1.7906507634  0.7732945315 -0.5883854056  0.1532221925  2.9340675863
 [841]  0.6956708544  0.0050612897  0.9769538891  0.4206629784 -1.2125499933  1.3072992060
 [847] -1.2183491932  3.2025533950  1.7151822722  0.2373791941  0.3447647574  0.4058147137
 [853]  0.3515136497  2.1526403238 -0.8506492379  1.0364765734  0.3865411653 -1.7882442554
 [859]  1.7748361890  0.9906405839  1.3020345160  0.5955496863  0.5457307513  1.8754952674
 [865] -0.0774433587 -0.3567431399 -0.9202812047  0.2059832127 -1.0085861309  1.6453466384
 [871]  0.0199185183 -0.4114969002  0.4124374655  1.8441385891  1.3060474774  2.4414523623
 [877]  1.6356735336  0.2247025734 -0.8256529998  1.3927874983 -0.2855332156  0.4579730717
 [883]  0.2012303476 -0.4606660268 -0.0849199257 -0.8234258153 -2.2031492519  0.6283613943
 [889]  0.7705917411  1.2686908966 -1.9340583216 -2.6126715326  1.2016443933  2.4716885374
 [895]  3.0084934304 -0.1015977879 -0.1417021212  0.6196411934  0.7428805756  1.9066467243
 [901]  0.1113479372  1.3453782577  1.3833733368 -0.6577774955 -1.0303768196 -0.7374567250
 [907]  1.2905629354 -0.2028427007  0.7027244506  1.4937738430 -1.1237486442  1.2610660099
 [913] -0.3586307614 -0.4547701881  1.3979107543 -1.3570961445  2.1528932172  2.2804794613
 [919] -0.8643531572  1.3960911211  0.0824611215  2.5008499695  2.8419640831  0.2119002431
 [925] -1.4391686672  0.1823666538  1.2436496268 -0.5369410926 -0.7584228057  2.9494597966
 [931] -1.6554809283 -0.9376363047  2.1010458671 -2.1063119756 -0.9510426263 -1.2017792891
 [937]  0.0295111848 -1.0011737272 -0.4221397525 -1.0616581425  0.7982243624  0.2201587331
 [943]  2.2814140182 -0.5222602845  1.6280989722 -1.2594466621 -0.5229734545  0.3596705107
 [949] -0.4041599207  0.3548243853  1.7627068592  0.1773272947  1.7545539298  0.6625986469
 [955]  2.3359719448  0.5630055207  0.9538549011  2.1788300642 -1.1085898904  0.0017850469
 [961]  0.3393884896  1.1852244893  3.9489005562 -0.3998921695 -1.1969670549 -0.4777765108
 [967] -0.8071927806  0.3148183004  0.7031127721  1.0381593101 -0.7575046324 -1.6373493767
 [973] -1.7227348709  0.7099154446  0.5986079288 -1.6222663160  0.0931507957 -0.0688222765
 [979] -0.8552166780  4.7022737392  0.0525521459 -0.3278999228  0.0436755526 -0.4055744379
 [985]  2.0269610152  0.5912839499  2.7216087265 -0.8356076734  0.0979750923  1.0018415298
 [991]  2.0612516737  0.5568348496  1.8768915325 -0.1314466840  0.1193293651 -0.1570242381
 [997]  2.5352018620 -0.9636077705  0.7184214244  0.2247693278
 [ reached getOption("max.print") -- omitted 11897 entries ]

$rawpval
   [1] 2.297362e-01 1.438748e-02 2.418938e-03 2.395465e-01 6.803138e-01 5.744559e-01
   [7] 6.019394e-02 9.014474e-01 6.802660e-02 5.124887e-01 5.377516e-01 1.390872e-02
  [13] 6.433832e-01 8.292821e-01 4.660754e-01 6.352230e-01 9.739750e-02 3.674145e-01
  [19] 6.893895e-02 2.612661e-01 4.332094e-01 6.097343e-01 9.089050e-01 9.509384e-01
  [25] 2.708402e-01 1.323838e-03 2.045582e-01 2.775257e-02 2.649825e-02 7.947425e-01
  [31] 1.968773e-01 1.296674e-01 1.595304e-01 2.224586e-03 7.026951e-01 4.788679e-01
  [37] 4.364250e-02 7.006659e-01 2.561914e-01 7.468133e-03 4.401648e-01 1.778511e-01
  [43] 9.215331e-01 3.009024e-01 9.018260e-01 4.706864e-03 2.701026e-02 7.082872e-01
  [49] 1.893641e-01 1.460397e-01 1.122805e-01 8.068737e-01 9.998085e-01 1.266728e-02
  [55] 4.979802e-01 1.592174e-01 4.482327e-01 7.434632e-03 7.588375e-02 8.896252e-01
  [61] 2.756769e-01 8.400040e-01 8.947701e-01 1.694371e-01 7.837500e-01 7.456736e-02
  [67] 3.315010e-01 9.537886e-03 7.900066e-01 6.503567e-01 3.724893e-01 3.821212e-01
  [73] 6.183004e-02 9.495728e-01 7.574071e-02 4.415953e-02 5.658189e-01 1.037449e-02
  [79] 6.034922e-01 8.772952e-02 7.392964e-01 4.835405e-01 1.271338e-01 3.293969e-01
  [85] 2.879245e-01 2.900609e-01 1.987066e-01 4.622462e-01 1.981461e-01 2.540880e-01
  [91] 9.941476e-01 6.954384e-02 2.712334e-01 5.278652e-01 8.577533e-01 1.235888e-03
  [97] 2.996409e-01 5.254972e-01 6.430733e-01 2.959891e-01 2.267871e-01 2.627457e-01
 [103] 2.346847e-01 9.089671e-01 1.845099e-01 3.214365e-01 5.308692e-01 5.615089e-01
 [109] 9.734578e-01 8.706307e-01 8.694735e-01 3.228434e-01 4.708095e-01 4.655582e-01
 [115] 9.512165e-01 3.106099e-01 8.686905e-01 1.394037e-02 5.243758e-01 5.984934e-01
 [121] 1.833713e-01 1.596746e-01 8.514924e-01 3.368331e-01 9.511699e-01 3.286690e-01
 [127] 1.395703e-04 2.585449e-01 5.945713e-01 5.251078e-01 5.157624e-01 7.150446e-01
 [133] 9.480250e-01 1.692083e-01 8.390612e-01 4.502648e-01 5.280477e-01 5.462636e-05
 [139] 3.369760e-05 1.339269e-02 3.885303e-01 4.876538e-01 3.545273e-01 9.745464e-01
 [145] 2.761220e-02 5.081310e-03 5.217267e-02 4.061188e-01 2.660921e-01 2.331022e-02
 [151] 5.825896e-01 3.307207e-01 9.102841e-01 4.815618e-01 6.676208e-01 7.150436e-01
 [157] 4.197763e-02 3.677138e-02 3.174521e-01 5.533740e-01 7.279172e-02 1.618452e-02
 [163] 4.458312e-04 1.906131e-02 1.938822e-01 5.911845e-01 2.422895e-01 5.363743e-01
 [169] 1.140048e-01 2.708543e-01 2.828190e-01 7.436360e-01 9.764219e-01 2.187883e-01
 [175] 8.260427e-02 8.975399e-01 5.334712e-03 1.373783e-01 1.356553e-01 3.436335e-01
 [181] 3.619807e-01 2.256411e-01 9.175055e-01 4.908216e-01 5.440564e-02 8.059920e-03
 [187] 3.961908e-01 8.182330e-01 3.168748e-01 1.100372e-01 6.554095e-01 4.921357e-01
 [193] 9.749656e-01 8.973913e-01 9.567945e-01 9.905333e-01 7.751021e-01 3.364086e-02
 [199] 3.436659e-01 2.397868e-01 4.298851e-01 2.070194e-01 3.241106e-01 4.822623e-02
 [205] 1.717994e-01 2.131401e-02 4.818618e-01 2.357619e-02 1.331611e-02 7.931341e-01
 [211] 4.354940e-01 6.631985e-01 9.700097e-02 2.974237e-01 9.859162e-01 9.950051e-02
 [217] 6.700121e-01 6.902808e-01 1.266036e-04 2.204661e-01 8.454934e-02 1.544063e-01
 [223] 1.900979e-01 3.013986e-01 8.579494e-01 2.640226e-01 1.358200e-01 4.266037e-01
 [229] 1.023616e-03 3.428316e-02 9.491041e-01 6.309683e-01 1.635580e-01 2.899398e-01
 [235] 9.774180e-02 1.579086e-01 4.814566e-01 1.714013e-01 3.831454e-02 3.645573e-01
 [241] 3.504372e-03 4.833976e-01 3.091451e-01 9.702872e-01 7.216039e-01 5.231859e-01
 [247] 2.618394e-01 2.324137e-02 8.384906e-01 6.481565e-03 3.374856e-01 1.619416e-01
 [253] 2.806998e-01 6.257654e-02 1.620362e-02 6.246656e-02 1.162566e-01 2.564443e-01
 [259] 6.611921e-01 2.665593e-01 5.549479e-01 4.431684e-02 7.148442e-01 6.050469e-01
 [265] 2.703807e-01 6.952876e-01 6.860872e-09 3.033496e-01 1.265971e-01 3.737152e-01
 [271] 6.164458e-01 5.353578e-01 9.609979e-01 5.607331e-01 6.321106e-01 5.260331e-01
 [277] 6.078631e-01 2.765306e-01 1.987417e-01 5.032330e-01 2.333990e-01 5.244330e-01
 [283] 9.373197e-02 6.921546e-01 7.681489e-01 5.265544e-03 6.134194e-01 5.057441e-01
 [289] 7.078137e-03 4.063326e-01 3.943701e-01 1.173930e-09 1.716998e-01 1.170139e-01
 [295] 1.861370e-03 7.226921e-01 5.421520e-01 5.454907e-01 5.966179e-01 5.846011e-01
 [301] 9.952999e-01 6.958889e-01 2.956993e-01 9.221357e-01 1.250575e-01 5.953141e-01
 [307] 5.590784e-01 9.804945e-01 4.395760e-01 9.041425e-01 4.886219e-01 8.555763e-01
 [313] 9.222643e-01 4.758795e-01 7.847738e-01 9.224935e-01 9.982303e-01 7.525035e-01
 [319] 3.786149e-01 1.513593e-01 8.925556e-01 7.917864e-01 5.366045e-01 1.045666e-01
 [325] 8.234831e-01 1.135533e-01 4.566461e-01 4.302019e-01 2.589870e-01 6.635876e-02
 [331] 6.756163e-01 9.792211e-01 5.632611e-01 2.803753e-01 6.827599e-01 5.145972e-01
 [337] 3.923330e-01 1.994515e-01 1.356742e-01 6.560800e-01 1.115903e-02 8.308284e-01
 [343] 5.679391e-01 5.488791e-02 5.503675e-01 7.327778e-01 5.633425e-02 6.281934e-02
 [349] 2.202961e-01 2.007061e-06 3.451125e-01 2.716047e-01 6.459577e-01 1.991888e-01
 [355] 3.894399e-01 8.306226e-01 2.548712e-02 3.026636e-03 3.442058e-01 8.073284e-01
 [361] 3.766791e-01 3.019557e-02 3.451503e-01 2.552119e-02 5.197396e-03 4.774274e-01
 [367] 1.060488e-06 5.852863e-01 1.638416e-02 7.242882e-01 3.115234e-01 5.003962e-01
 [373] 9.172737e-02 5.929646e-03 1.725867e-01 3.718132e-01 4.704150e-01 1.318633e-01
 [379] 2.280339e-01 8.849037e-01 8.927659e-02 6.622883e-01 8.539595e-01 6.594391e-03
 [385] 3.884887e-01 5.512266e-01 1.816066e-01 2.237663e-02 4.926742e-02 9.361461e-02
 [391] 5.588314e-02 3.823821e-01 7.464571e-01 3.709312e-01 3.434030e-01 4.546018e-01
 [397] 6.848558e-01 4.005739e-02 4.748693e-01 7.598220e-01 3.628333e-02 9.221423e-01
 [403] 5.414129e-01 4.880473e-02 2.078755e-04 1.129871e-04 4.633054e-01 7.793430e-03
 [409] 7.828980e-01 8.964393e-01 9.959758e-01 4.391732e-01 5.826182e-01 1.879184e-01
 [415] 4.633299e-01 3.983220e-01 1.760187e-01 7.425209e-03 1.289162e-04 3.329385e-01
 [421] 1.794365e-01 3.980192e-03 3.839961e-02 6.719181e-01 6.460209e-01 5.645146e-03
 [427] 6.575888e-02 3.153041e-01 9.221274e-02 5.259348e-01 8.571223e-02 3.831627e-01
 [433] 3.578889e-01 2.718983e-01 1.808869e-01 7.017467e-02 8.659628e-01 9.598502e-02
 [439] 9.348163e-02 5.024062e-02 2.075842e-01 2.202889e-01 5.453735e-02 9.214141e-01
 [445] 1.695063e-01 6.338460e-01 6.936927e-01 7.316785e-01 1.391534e-01 8.482471e-01
 [451] 1.873377e-01 2.773731e-01 5.728894e-01 7.879219e-01 1.216839e-04 2.874016e-01
 [457] 5.215611e-01 1.372706e-01 3.852390e-01 2.702170e-01 9.353889e-01 6.712189e-01
 [463] 7.799350e-01 9.161653e-02 9.071349e-01 3.971529e-03 2.307542e-01 7.117292e-01
 [469] 5.838015e-03 3.043834e-01 1.969665e-02 2.754372e-02 3.399588e-01 2.045373e-02
 [475] 2.764411e-03 3.935153e-01 8.438783e-01 1.317544e-01 1.886961e-02 7.176138e-01
 [481] 5.546052e-01 7.216213e-01 2.398975e-01 8.718597e-01 9.962328e-03 2.049780e-01
 [487] 6.604311e-02 8.931735e-01 6.696451e-02 6.354126e-01 8.058066e-01 3.314506e-01
 [493] 1.795866e-01 7.341446e-01 5.336804e-01 8.459747e-03 3.005965e-01 1.368419e-01
 [499] 1.050987e-01 3.896303e-02 7.829387e-01 6.953403e-01 9.671258e-01 8.644304e-01
 [505] 3.232832e-01 1.904392e-02 9.992927e-01 3.211843e-01 2.847294e-01 1.060657e-01
 [511] 4.204257e-01 4.494406e-01 6.370518e-01 6.733380e-01 3.571633e-01 6.813795e-01
 [517] 8.846818e-01 8.545517e-01 2.314348e-01 9.806171e-01 4.840043e-02 7.515926e-01
 [523] 2.865278e-01 7.634867e-01 9.725773e-01 4.587463e-03 3.287430e-01 1.033262e-01
 [529] 5.056561e-01 7.913969e-02 2.283976e-01 9.742827e-01 2.566686e-02 3.392304e-04
 [535] 7.910252e-01 5.871700e-01 2.175016e-01 4.628758e-01 4.186937e-02 9.701207e-01
 [541] 1.900726e-02 2.745168e-02 7.849596e-01 1.061431e-02 3.032499e-02 2.647920e-01
 [547] 2.174019e-03 3.446804e-02 2.075318e-02 1.236085e-01 8.384032e-01 2.212040e-04
 [553] 2.972174e-01 7.171595e-01 2.423232e-01 5.271792e-01 6.011221e-01 2.222409e-01
 [559] 1.423999e-03 2.528696e-01 9.401893e-01 1.408592e-02 5.188346e-04 2.165108e-01
 [565] 4.380299e-01 1.529601e-05 1.125431e-01 5.779248e-02 3.444229e-02 1.567585e-01
 [571] 5.473278e-03 3.155677e-01 3.588736e-01 2.776043e-03 8.160578e-02 1.891535e-02
 [577] 7.289945e-03 2.057201e-01 7.887232e-01 4.376513e-01 2.348451e-02 2.662110e-02
 [583] 8.751285e-01 1.646014e-01 6.962858e-01 2.108314e-01 3.753758e-01 7.027556e-02
 [589] 3.869808e-01 1.505562e-01 2.913116e-01 5.089306e-01 1.080257e-01 6.568243e-01
 [595] 3.569780e-02 4.590105e-01 1.346291e-01 2.199995e-01 1.290179e-01 1.549410e-01
 [601] 6.837648e-01 3.506640e-01 5.914016e-01 1.427781e-01 5.327250e-01 2.340189e-01
 [607] 2.179230e-03 9.366848e-01 5.152246e-02 6.795651e-01 1.025138e-01 6.413371e-02
 [613] 3.418101e-01 4.878443e-01 2.788747e-01 9.836966e-02 2.264095e-01 4.742446e-01
 [619] 2.534168e-01 4.558619e-01 9.851639e-01 1.833765e-01 3.194571e-01 6.716448e-01
 [625] 1.893155e-01 5.029827e-01 3.779483e-01 7.013998e-01 7.740834e-04 9.317111e-02
 [631] 9.550877e-01 6.361233e-01 6.397497e-01 2.165703e-01 9.385428e-01 9.349103e-02
 [637] 1.687071e-01 9.331070e-02 2.603385e-01 4.545129e-02 9.186630e-01 4.382567e-01
 [643] 2.432492e-01 1.542406e-01 1.744001e-03 4.456181e-01 1.083093e-01 7.083246e-01
 [649] 9.698222e-01 1.761637e-01 2.524586e-02 1.262080e-02 1.214102e-02 6.001844e-01
 [655] 8.786557e-01 8.219016e-02 6.402652e-01 3.732423e-01 6.228952e-02 9.857045e-01
 [661] 3.261078e-01 1.038608e-01 5.944232e-01 6.808667e-01 5.333719e-01 5.205855e-01
 [667] 6.641504e-01 2.874788e-01 5.777033e-02 8.033094e-05 4.166406e-01 4.560570e-02
 [673] 8.776398e-01 4.937509e-01 2.574860e-01 4.213183e-01 5.952587e-01 8.900186e-01
 [679] 1.128060e-01 4.416156e-02 5.590950e-02 5.391481e-01 7.299540e-01 7.143096e-03
 [685] 6.943848e-01 8.398398e-01 5.736833e-01 8.472133e-01 6.278477e-02 2.963415e-01
 [691] 7.878878e-01 3.456269e-01 8.629748e-02 9.097064e-02 4.307169e-01 3.812751e-01
 [697] 4.876122e-01 9.210119e-01 2.358610e-01 9.438355e-01 3.189135e-02 8.469153e-02
 [703] 2.070938e-01 1.311487e-01 1.261810e-03 4.954860e-01 3.020071e-01 1.682151e-03
 [709] 2.398410e-05 2.463920e-02 5.470827e-01 1.977007e-02 7.291473e-01 2.090240e-01
 [715] 2.951328e-03 9.635881e-01 2.741956e-01 8.294017e-01 2.232005e-01 3.879701e-01
 [721] 7.322247e-01 2.192512e-02 4.267732e-01 1.964708e-01 2.191001e-02 4.913012e-01
 [727] 4.591428e-01 9.770180e-01 5.093202e-01 8.458056e-01 6.088737e-01 6.156819e-01
 [733] 6.036879e-01 1.096725e-01 7.438250e-02 8.373330e-01 5.951245e-01 2.043948e-01
 [739] 4.087158e-01 6.682392e-01 3.717709e-01 9.689801e-01 2.903103e-01 4.900939e-01
 [745] 5.595750e-02 3.571613e-02 2.370574e-01 9.350255e-02 9.890084e-01 8.776623e-01
 [751] 2.572620e-01 5.305424e-01 4.132907e-01 1.789933e-01 7.816557e-01 6.199981e-01
 [757] 1.137063e-01 4.166983e-01 6.934785e-01 4.908468e-01 2.385336e-01 5.437296e-02
 [763] 9.475889e-01 7.108011e-02 1.781720e-01 8.927851e-01 9.690075e-01 5.982834e-02
 [769] 6.625831e-01 6.849443e-01 6.441519e-01 6.989952e-01 8.423069e-01 1.345792e-01
 [775] 9.809976e-01 1.988344e-01 5.343409e-03 9.571588e-01 1.817799e-01 2.960564e-01
 [781] 1.331078e-01 5.640582e-01 2.559740e-02 6.548120e-01 3.935203e-01 7.869854e-01
 [787] 3.935652e-02 3.497422e-01 8.082209e-01 2.974881e-01 1.638802e-01 9.276363e-06
 [793] 9.554606e-01 1.727521e-03 6.648104e-01 8.020259e-01 9.817970e-01 6.829862e-01
 [799] 2.393754e-01 1.181444e-01 2.569312e-01 5.325935e-01 4.968040e-01 2.302003e-02
 [805] 9.350840e-01 1.645869e-02 4.506237e-01 9.524001e-01 5.059937e-02 9.322043e-01
 [811] 7.367239e-01 5.485705e-02 1.278518e-01 5.120426e-01 7.939610e-01 4.904157e-01
 [817] 2.053043e-01 2.294200e-01 1.188523e-01 5.689217e-01 7.734919e-01 8.131113e-01
 [823] 3.975920e-07 3.902683e-02 7.989556e-01 9.181199e-01 4.389980e-02 6.960699e-05
 [829] 2.162414e-01 5.718765e-02 6.065178e-01 5.993480e-02 9.643203e-02 1.416021e-01
 [835] 7.256776e-01 3.667468e-02 2.196740e-01 7.218632e-01 4.391115e-01 1.672757e-03
 [841] 2.433175e-01 4.979808e-01 1.642960e-01 3.370006e-01 8.873490e-01 9.555556e-02
 [847] 8.884543e-01 6.810752e-04 4.315591e-02 4.061813e-01 3.651356e-01 3.424394e-01
 [853] 3.626015e-01 1.567348e-02 8.025179e-01 1.499899e-01 3.495480e-01 9.631317e-01
 [859] 3.796247e-02 1.609306e-01 9.645229e-02 2.757380e-01 2.926255e-01 3.036231e-02
 [865] 5.308646e-01 6.393579e-01 8.212871e-01 4.184020e-01 8.434134e-01 4.994917e-02
 [871] 4.920542e-01 6.596459e-01 3.400094e-01 3.258147e-02 9.576821e-02 7.314159e-03
 [877] 5.095397e-02 4.111053e-01 7.954995e-01 8.184203e-02 6.123822e-01 3.234859e-01
 [883] 4.202592e-01 6.774809e-01 5.338375e-01 7.948671e-01 9.862079e-01 2.648836e-01
 [889] 2.204745e-01 1.022757e-01 9.734470e-01 9.955081e-01 1.147507e-01 6.723831e-03
 [895] 1.312732e-03 5.404620e-01 5.563424e-01 2.677470e-01 2.287770e-01 2.828318e-02
 [901] 4.556702e-01 8.925156e-02 8.327521e-02 7.446594e-01 8.485834e-01 7.695777e-01
 [907] 9.842764e-02 5.803710e-01 2.411137e-01 6.761737e-02 8.694402e-01 1.036425e-01
 [913] 6.400643e-01 6.753627e-01 8.106993e-02 9.126247e-01 1.566354e-02 1.128963e-02
 [919] 8.063030e-01 8.134353e-02 4.671400e-01 6.194783e-03 2.241827e-03 4.160924e-01
 [925] 9.249486e-01 4.276475e-01 1.068143e-01 7.043458e-01 7.759010e-01 1.591650e-03
 [931] 9.510865e-01 8.257843e-01 1.781847e-02 9.824114e-01 8.292086e-01 8.852755e-01
 [937] 4.882284e-01 8.416286e-01 6.635385e-01 8.558045e-01 2.123702e-01 4.128738e-01
 [943] 1.126198e-02 6.992554e-01 5.175195e-02 8.960655e-01 6.995036e-01 3.595468e-01
 [949] 6.569524e-01 3.613606e-01 3.897497e-02 4.296257e-01 3.966782e-02 2.537938e-01
 [955] 9.746352e-03 2.867156e-01 1.700785e-01 1.467215e-02 8.661964e-01 4.992879e-01
 [961] 3.671585e-01 1.179643e-01 3.925548e-05 6.553820e-01 8.843403e-01 6.835954e-01
 [967] 7.902223e-01 3.764498e-01 2.409927e-01 1.495979e-01 7.756262e-01 9.492213e-01
 [973] 9.575318e-01 2.388783e-01 2.747172e-01 9.476268e-01 4.628919e-01 5.274345e-01
 [979] 8.037844e-01 1.286401e-06 4.790444e-01 6.285063e-01 4.825815e-01 6.574723e-01
 [985] 2.133320e-02 2.771651e-01 3.248251e-03 7.983122e-01 4.609760e-01 1.582101e-01
 [991] 1.963952e-02 2.888201e-01 3.026648e-02 5.522890e-01 4.525072e-01 5.623871e-01
 [997] 5.619127e-03 8.323787e-01 2.362487e-01 4.110794e-01
 [ reached getOption("max.print") -- omitted 11897 entries ]

$adjpval
   [1] 5.989089e-01 1.564548e-01 6.093174e-02 6.091150e-01 8.809048e-01 8.321642e-01
   [7] 3.271693e-01 9.664881e-01 3.477365e-01 8.050812e-01 8.158484e-01 1.547720e-01
  [13] 8.660591e-01 9.412877e-01 7.765970e-01 8.627460e-01 4.104599e-01 7.117069e-01
  [19] 3.500416e-01 6.285299e-01 7.558236e-01 8.491246e-01 9.696401e-01 9.860164e-01
  [25] 6.389943e-01 4.322415e-02 5.709880e-01 2.217626e-01 2.167076e-01 9.258236e-01
  [31] 5.651294e-01 4.679130e-01 5.145735e-01 5.751119e-02 8.903266e-01 7.854272e-01
  [37] 2.814930e-01 8.898956e-01 6.241620e-01 1.104628e-01 7.604562e-01 5.402578e-01
  [43] 9.743411e-01 6.636008e-01 9.667401e-01 8.702076e-02 2.181286e-01 8.942113e-01
  [49] 5.542734e-01 4.947396e-01 4.397000e-01 9.303788e-01 9.999636e-01 1.463888e-01
  [55] 7.967205e-01 5.144919e-01 7.677291e-01 1.104628e-01 3.658059e-01 9.625886e-01
  [61] 6.422959e-01 9.461840e-01 9.642356e-01 5.288508e-01 9.227895e-01 3.628623e-01
  [67] 6.843861e-01 1.255864e-01 9.244676e-01 8.684045e-01 7.154399e-01 7.228975e-01
  [73] 3.315684e-01 9.856451e-01 3.655793e-01 2.823982e-01 8.294719e-01 1.299027e-01
  [79] 8.454529e-01 3.936492e-01 9.066856e-01 7.884969e-01 4.650597e-01 6.833867e-01
  [85] 6.540886e-01 6.549222e-01 5.662361e-01 7.747355e-01 5.662361e-01 6.223360e-01
  [91] 9.981722e-01 3.518662e-01 6.391323e-01 8.124717e-01 9.532481e-01 4.216734e-02
  [97] 6.628130e-01 8.104925e-01 8.659619e-01 6.600010e-01 5.963044e-01 6.304325e-01
 [103] 6.030542e-01 9.696401e-01 5.480611e-01 6.781029e-01 8.139228e-01 8.275079e-01
 [109] 9.908078e-01 9.574066e-01 9.572034e-01 6.789232e-01 7.800291e-01 7.762513e-01
 [115] 9.860164e-01 6.697493e-01 9.570588e-01 1.548570e-01 8.099073e-01 8.423795e-01
 [121] 5.465696e-01 5.145735e-01 9.509371e-01 6.885167e-01 9.860164e-01 6.832034e-01
 [127] 1.125815e-02 6.263309e-01 8.408717e-01 8.101727e-01 8.062773e-01 8.965514e-01
 [133] 9.848328e-01 5.287810e-01 9.457511e-01 7.682320e-01 8.124717e-01 6.234656e-03
 [139] 4.709817e-03 1.511159e-01 7.267553e-01 7.912520e-01 7.039782e-01 9.908693e-01
 [145] 2.211891e-01 9.152744e-02 3.055726e-01 7.371871e-01 6.339905e-01 2.042337e-01
 [151] 8.352114e-01 6.838712e-01 9.703570e-01 7.873006e-01 8.742314e-01 8.965514e-01
 [157] 2.756545e-01 2.569017e-01 6.756429e-01 8.227310e-01 3.586628e-01 1.663746e-01
 [163] 2.346892e-02 1.823653e-01 5.611533e-01 8.389306e-01 6.113118e-01 8.151182e-01
 [169] 4.415586e-01 6.389943e-01 6.489088e-01 9.090459e-01 9.916430e-01 5.874897e-01
 [175] 3.828875e-01 9.647918e-01 9.412008e-02 4.807690e-01 4.786080e-01 6.936243e-01
 [181] 7.083716e-01 5.944629e-01 9.727942e-01 7.929915e-01 3.125477e-01 1.156271e-01
 [187] 7.309976e-01 9.365507e-01 6.756209e-01 4.358568e-01 8.698971e-01 7.935333e-01
 [193] 9.908693e-01 9.647918e-01 9.870005e-01 9.961719e-01 9.198943e-01 2.475871e-01
 [199] 6.936243e-01 6.093232e-01 7.534341e-01 5.743848e-01 6.790984e-01 2.956148e-01
 [205] 5.312146e-01 1.952321e-01 7.873524e-01 2.058647e-01 1.506473e-01 9.253446e-01
 [211] 7.575488e-01 8.723079e-01 4.100813e-01 6.610773e-01 9.948562e-01 4.148910e-01
 [217] 8.749367e-01 8.860905e-01 1.074215e-02 5.887199e-01 3.870901e-01 5.084045e-01
 [223] 5.553096e-01 6.643544e-01 9.533017e-01 6.318610e-01 4.789911e-01 7.507038e-01
 [229] 3.754079e-02 2.494353e-01 9.854671e-01 8.602683e-01 5.209494e-01 6.548781e-01
 [235] 4.113157e-01 5.123390e-01 7.872887e-01 5.310041e-01 2.626549e-01 7.098307e-01
 [241] 7.514852e-02 7.884320e-01 6.682119e-01 9.900937e-01 8.992436e-01 8.094688e-01
 [247] 6.289705e-01 2.040462e-01 9.455626e-01 1.033640e-01 6.891193e-01 5.179964e-01
 [253] 6.473864e-01 3.336771e-01 1.663838e-01 3.336620e-01 4.462385e-01 6.243841e-01
 [259] 8.718326e-01 6.347056e-01 8.235272e-01 2.827780e-01 8.965514e-01 8.464663e-01
 [265] 6.381954e-01 8.879885e-01 1.279365e-05 6.656231e-01 4.639735e-01 7.160673e-01
 [271] 8.532691e-01 8.146914e-01 9.881989e-01 8.269612e-01 8.607676e-01 8.106808e-01
 [277] 8.482591e-01 6.431482e-01 5.662361e-01 7.995100e-01 6.022626e-01 8.099073e-01
 [283] 4.036264e-01 8.870832e-01 9.182542e-01 9.365706e-02 8.517002e-01 8.012267e-01
 [289] 1.084165e-01 7.373677e-01 7.292363e-01 5.046723e-06 5.311614e-01 4.476001e-01
 [295] 5.184900e-02 8.997548e-01 8.178892e-01 8.197161e-01 8.417658e-01 8.362166e-01
 [301] 9.983255e-01 8.879885e-01 6.600010e-01 9.746374e-01 4.612449e-01 8.409734e-01
 [307] 8.260320e-01 9.935155e-01 7.602206e-01 9.678557e-01 7.914791e-01 9.524762e-01
 [313] 9.746374e-01 7.831335e-01 9.228802e-01 9.746374e-01 9.992375e-01 9.113275e-01
 [319] 7.208072e-01 5.037061e-01 9.634966e-01 9.247624e-01 8.151182e-01 4.240139e-01
 [325] 9.385267e-01 4.412086e-01 7.718228e-01 7.535399e-01 6.269060e-01 3.439164e-01
 [331] 8.782808e-01 9.931484e-01 8.280381e-01 6.471721e-01 8.819666e-01 8.059393e-01
 [337] 7.282555e-01 5.664832e-01 4.786080e-01 8.704314e-01 1.352613e-01 9.417755e-01
 [343] 8.296546e-01 3.133969e-01 8.217759e-01 9.034160e-01 3.163825e-01 3.341876e-01
 [349] 5.887199e-01 7.613254e-04 6.945189e-01 6.393352e-01 8.666590e-01 5.662430e-01
 [355] 7.267553e-01 9.417755e-01 2.128934e-01 7.020597e-02 6.939538e-01 9.306502e-01
 [361] 7.189627e-01 2.330435e-01 6.945189e-01 2.129635e-01 9.284047e-02 7.839834e-01
 [367] 5.270142e-04 8.364152e-01 1.674378e-01 9.006986e-01 6.703250e-01 7.980227e-01
 [373] 4.006557e-01 9.893227e-02 5.323929e-01 7.148592e-01 7.798127e-01 4.718759e-01
 [379] 5.975542e-01 9.612232e-01 3.962148e-01 8.722717e-01 9.519851e-01 1.042253e-01
 [385] 7.267553e-01 8.223405e-01 5.445105e-01 2.002716e-01 2.973640e-01 4.033904e-01
 [391] 3.157494e-01 7.229301e-01 9.106802e-01 7.140149e-01 6.936243e-01 7.706065e-01
 [397] 8.831699e-01 2.674774e-01 7.829937e-01 9.142960e-01 2.554724e-01 9.746374e-01
 [403] 8.177307e-01 2.962954e-01 1.497749e-02 1.026194e-02 7.752269e-01 1.140884e-01
 [409] 9.227415e-01 9.645920e-01 9.984531e-01 7.599436e-01 8.352114e-01 5.523467e-01
 [415] 7.752269e-01 7.320746e-01 5.377029e-01 1.104532e-01 1.082529e-02 6.851617e-01
 [421] 5.424735e-01 8.083864e-02 2.627871e-01 8.760339e-01 8.666590e-01 9.681576e-02
 [427] 3.429407e-01 6.741526e-01 4.009720e-01 8.106808e-01 3.899059e-01 7.234769e-01
 [433] 7.058196e-01 6.393352e-01 5.442546e-01 3.535052e-01 9.562022e-01 4.089590e-01
 [439] 4.033904e-01 3.005035e-01 5.751263e-01 5.887199e-01 3.126081e-01 9.742951e-01
 [445] 5.288508e-01 8.618647e-01 8.876431e-01 9.030795e-01 4.837663e-01 9.500466e-01
 [451] 5.519707e-01 6.442192e-01 8.314826e-01 9.237331e-01 1.060377e-02 6.538513e-01
 [457] 8.084703e-01 4.807690e-01 7.251062e-01 6.379258e-01 9.795948e-01 8.755648e-01
 [463] 9.222463e-01 4.006149e-01 9.695300e-01 8.083864e-02 6.003706e-01 8.958785e-01
 [469] 9.803760e-02 6.663754e-01 1.865108e-01 2.210409e-01 6.907216e-01 1.914309e-01
 [475] 6.616665e-02 7.289084e-01 9.478191e-01 4.718759e-01 1.816997e-01 8.975043e-01
 [481] 8.233730e-01 8.992436e-01 6.094067e-01 9.575203e-01 1.279723e-01 5.717130e-01
 [487] 3.435148e-01 9.637127e-01 3.449046e-01 8.627460e-01 9.300164e-01 6.843861e-01
 [493] 5.426729e-01 9.041435e-01 8.146653e-01 1.179517e-01 6.634396e-01 4.802312e-01
 [499] 4.250417e-01 2.640761e-01 9.227415e-01 8.879885e-01 9.893998e-01 9.553996e-01
 [505] 6.789232e-01 1.823381e-01 9.997578e-01 6.780674e-01 6.513223e-01 4.275882e-01
 [511] 7.455493e-01 7.677291e-01 8.632691e-01 8.769977e-01 7.055863e-01 8.809775e-01
 [517] 9.612232e-01 9.521933e-01 6.009261e-01 9.935155e-01 2.956893e-01 9.112296e-01
 [523] 6.536916e-01 9.158006e-01 9.907843e-01 8.612010e-02 6.832034e-01 4.222429e-01
 [529] 8.012267e-01 3.744184e-01 5.981003e-01 9.908693e-01 2.134272e-01 2.006906e-02
 [535] 9.247509e-01 8.368235e-01 5.859867e-01 7.750024e-01 2.751871e-01 9.900021e-01
 [541] 1.821223e-01 2.207331e-01 9.229304e-01 1.313284e-01 2.331322e-01 6.329820e-01
 [547] 5.712505e-02 2.494353e-01 1.932518e-01 4.586245e-01 9.455626e-01 1.563052e-02
 [553] 6.610773e-01 8.971102e-01 6.113118e-01 8.118246e-01 8.436018e-01 5.905664e-01
 [559] 4.539924e-02 6.216487e-01 9.812158e-01 1.554030e-01 2.506417e-02 5.851295e-01
 [565] 7.592473e-01 3.034964e-03 4.401055e-01 3.201674e-01 2.494353e-01 5.105338e-01
 [571] 9.551944e-02 6.741526e-01 7.068407e-01 6.617319e-02 3.809156e-01 1.817819e-01
 [577] 1.100918e-01 5.722978e-01 9.239022e-01 7.591188e-01 2.053422e-01 2.169299e-01
 [583] 9.587306e-01 5.218447e-01 8.879885e-01 5.779526e-01 7.178676e-01 3.535771e-01
 [589] 7.258132e-01 5.026932e-01 6.567114e-01 8.033877e-01 4.313335e-01 8.705432e-01
 [595] 2.540519e-01 7.727409e-01 4.770186e-01 5.887199e-01 4.669307e-01 5.086896e-01
 [601] 8.823809e-01 7.004047e-01 8.389468e-01 4.894762e-01 8.146653e-01 6.024235e-01
 [607] 5.712505e-02 9.801944e-01 3.039731e-01 8.806622e-01 4.201210e-01 3.382955e-01
 [613] 6.925740e-01 7.912520e-01 6.456017e-01 4.129407e-01 5.956760e-01 7.828799e-01
 [619] 6.220841e-01 7.714892e-01 9.948562e-01 5.465696e-01 6.770810e-01 8.759432e-01
 [625] 5.542734e-01 7.995100e-01 7.204453e-01 8.898956e-01 3.129578e-02 4.028285e-01
 [631] 9.865330e-01 8.631349e-01 8.638900e-01 5.851295e-01 9.808270e-01 4.033904e-01
 [637] 5.282512e-01 4.030235e-01 6.277035e-01 2.865763e-01 9.732019e-01 7.592738e-01
 [643] 6.123709e-01 5.083065e-01 5.031854e-02 7.654684e-01 4.321309e-01 8.942113e-01
 [649] 9.899325e-01 5.377029e-01 2.117008e-01 1.463888e-01 1.424774e-01 8.434759e-01
 [655] 9.597716e-01 3.819843e-01 8.642977e-01 7.160673e-01 3.333816e-01 9.948562e-01
 [661] 6.804419e-01 4.233648e-01 8.408717e-01 8.809048e-01 8.146653e-01 8.080926e-01
 [667] 8.724330e-01 6.538513e-01 3.201674e-01 8.222445e-03 7.432192e-01 2.865763e-01
 [673] 9.595804e-01 7.941335e-01 6.251133e-01 7.464160e-01 8.409734e-01 9.625886e-01
 [679] 4.402266e-01 2.823982e-01 3.157494e-01 8.167040e-01 9.024938e-01 1.088953e-01
 [685] 8.879885e-01 9.461840e-01 8.320378e-01 9.493058e-01 3.341876e-01 6.600984e-01
 [691] 9.237331e-01 6.950804e-01 3.904456e-01 3.991999e-01 7.537166e-01 7.224957e-01
 [697] 7.912520e-01 9.741893e-01 6.046311e-01 9.829758e-01 2.389493e-01 3.872986e-01
 [703] 5.743848e-01 4.710179e-01 4.251851e-02 7.947936e-01 6.649618e-01 4.991882e-02
 [709] 3.986866e-03 2.099251e-01 8.201472e-01 1.869315e-01 9.023041e-01 5.768848e-01
 [715] 6.930865e-02 9.888487e-01 6.410563e-01 9.412877e-01 5.919632e-01 7.267466e-01
 [721] 9.030795e-01 1.980170e-01 7.507262e-01 5.649271e-01 1.980170e-01 7.933154e-01
 [727] 7.727409e-01 9.917829e-01 8.035110e-01 9.485122e-01 8.487125e-01 8.528030e-01
 [733] 8.454735e-01 4.353481e-01 3.628257e-01 9.451067e-01 8.409734e-01 5.709880e-01
 [739] 7.392998e-01 8.742421e-01 7.148592e-01 9.899325e-01 6.552557e-01 7.924049e-01
 [745] 3.157494e-01 2.540519e-01 6.062521e-01 4.033904e-01 9.961224e-01 9.595804e-01
 [751] 6.250332e-01 8.137404e-01 7.412488e-01 5.418667e-01 9.223914e-01 8.550018e-01
 [757] 4.413490e-01 7.432192e-01 8.876431e-01 7.929915e-01 6.078189e-01 3.124991e-01
 [763] 9.848328e-01 3.555533e-01 5.408540e-01 9.634966e-01 9.899325e-01 3.263003e-01
 [769] 8.722717e-01 8.831699e-01 8.664504e-01 8.898373e-01 9.470170e-01 4.770186e-01
 [775] 9.935155e-01 5.662361e-01 9.414473e-02 9.870005e-01 5.447569e-01 6.600010e-01
 [781] 4.739418e-01 8.287384e-01 2.132621e-01 8.698706e-01 7.289084e-01 9.235815e-01
 [787] 2.650554e-01 6.991050e-01 9.311517e-01 6.610773e-01 5.209494e-01 1.993954e-03
 [793] 9.865330e-01 5.010274e-02 8.726778e-01 9.290828e-01 9.937575e-01 8.820822e-01
 [799] 6.090402e-01 4.491531e-01 6.248498e-01 8.146653e-01 7.955204e-01 2.029199e-01
 [805] 9.795938e-01 1.678005e-01 7.685925e-01 9.862539e-01 3.014360e-01 9.783389e-01
 [811] 9.057292e-01 3.133969e-01 4.656606e-01 8.050812e-01 9.253751e-01 7.926053e-01
 [817] 5.720105e-01 5.984299e-01 4.502889e-01 8.296546e-01 9.196558e-01 9.336446e-01
 [823] 2.229454e-04 2.640761e-01 9.275956e-01 9.731018e-01 2.819600e-01 7.419185e-03
 [829] 5.849496e-01 3.180462e-01 8.470233e-01 3.265649e-01 4.093272e-01 4.880831e-01
 [835] 9.015571e-01 2.566431e-01 5.885202e-01 8.992436e-01 7.599436e-01 4.991882e-02
 [841] 6.123709e-01 7.967205e-01 5.216458e-01 6.885167e-01 9.623838e-01 4.083285e-01
 [847] 9.623838e-01 2.880129e-02 2.798299e-01 7.371968e-01 7.099584e-01 6.927201e-01
 [853] 7.084533e-01 1.644759e-01 9.290828e-01 5.020556e-01 6.989333e-01 9.887687e-01
 [859] 2.618192e-01 5.154819e-01 4.093272e-01 6.422959e-01 6.576048e-01 2.332238e-01
 [865] 8.139228e-01 8.637313e-01 9.377352e-01 7.446020e-01 9.477653e-01 2.997648e-01
 [871] 7.935333e-01 8.711786e-01 6.907216e-01 2.422948e-01 4.083725e-01 1.101035e-01
 [877] 3.018619e-01 7.399674e-01 9.262036e-01 3.814084e-01 8.510302e-01 6.789232e-01
 [883] 7.455493e-01 8.791377e-01 8.146653e-01 9.258592e-01 9.949203e-01 6.329820e-01
 [889] 5.887199e-01 4.201210e-01 9.908078e-01 9.983255e-01 4.432283e-01 1.053673e-01
 [895] 4.309808e-02 8.173196e-01 8.242757e-01 6.359021e-01 5.982398e-01 2.247493e-01
 [901] 7.714892e-01 3.962148e-01 3.845552e-01 9.098023e-01 9.500466e-01 9.186180e-01
 [907] 4.129407e-01 8.349186e-01 6.104523e-01 3.470628e-01 9.572034e-01 4.228655e-01
 [913] 8.641170e-01 8.780396e-01 3.795132e-01 9.710519e-01 1.644759e-01 1.360040e-01
 [919] 9.301856e-01 3.802419e-01 7.768084e-01 1.012600e-01 5.759531e-02 7.430551e-01
 [925] 9.757135e-01 7.518225e-01 4.288404e-01 8.916695e-01 9.202497e-01 4.841394e-02
 [931] 9.860164e-01 9.393315e-01 1.759571e-01 9.937575e-01 9.412877e-01 9.612596e-01
 [937] 7.914791e-01 9.469153e-01 8.723079e-01 9.524762e-01 5.804064e-01 7.410717e-01
 [943] 1.360040e-01 8.898574e-01 3.047694e-01 9.645920e-01 8.898956e-01 7.073288e-01
 [949] 8.705432e-01 7.081126e-01 2.640761e-01 7.533490e-01 2.660681e-01 6.222774e-01
 [955] 1.268120e-01 6.536916e-01 5.294480e-01 1.584813e-01 9.562022e-01 7.978337e-01
 [961] 7.117069e-01 4.491531e-01 5.012652e-03 8.698971e-01 9.611367e-01 8.822743e-01
 [967] 9.244676e-01 7.187377e-01 6.104523e-01 5.015245e-01 9.201776e-01 9.854671e-01
 [973] 9.870005e-01 6.082553e-01 6.415042e-01 9.848328e-01 7.750024e-01 8.121206e-01
 [979] 9.296310e-01 6.144710e-04 7.854355e-01 8.592163e-01 7.880619e-01 8.705432e-01
 [985] 1.952692e-01 6.439557e-01 7.263541e-02 9.273853e-01 7.740148e-01 5.128010e-01
 [991] 1.861064e-01 6.545270e-01 2.330435e-01 8.224639e-01 7.691072e-01 8.275079e-01
 [997] 9.649785e-02 9.426283e-01 6.051254e-01 7.399674e-01
 [ reached getOption("max.print") -- omitted 11897 entries ]

hist(invnormcomb$rawpval, breaks=100, col="grey", main="Inverse normal method", xlab = "Raw p-values (meta-analysis)")

## Summarize the results. DE are the results from the original anlayses of the datasets individually.
HC_Meadow_results_metastats <- data.frame(DE, "DE.fishercomb_adjP"=fishcomb$adjpval, "DE.invnorm_adj_P"=invnormcomb$adjpval)
library(data.table)
setDT(HC_Meadow_results_metastats, keep.rownames = "Gene")  # make the rownames a part of the dataframe with column name "Gene"
head(HC_Meadow_results_metastats)

HC_Meadow_results <- data.frame(DE, "DE.fishercomb"=ifelse(fishcomb$adjpval<=0.05,1,0), "DE.invnorm"=ifelse(invnormcomb$adjpval<=0.05,1,0))
head(HC_Meadow_results)

## Dealing with conflicts in the sign of differential expression 
signsFC <- mapply(HC_Meadow_FC, FUN=function(x) sign(x))
sumsigns <- apply(signsFC,1,sum)
commonsngFC <- ifelse(abs(sumsigns)==dim(signsFC)[2], sign(sumsigns), 0)
unionDE <- unique(c(fishcomb$DEindices, invnormcomb$DEindicies))
FC.selecDE <- data.frame(HC_Meadow_results[unionDE,], do.call(cbind,HC_Meadow_FC) [unionDE,], signFC=commonsngFC[unionDE], DE=DE[unionDE])
  # keep the ones that don't have a conflict (1 or -1)
keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),]
conflictDE <- FC.selecDE[which(FC.selecDE$signFC==0),]
head(FC.selecDE)  # the ones with conflict = 0

head(keepDE)

head(conflictDE)

table(conflictDE$DE)

 0 
22 

### Merge results and write to  a file
setDT(FC.selecDE, keep.rownames = "Gene")
head(FC.selecDE)

head(top.table_HC_Meadow)

head(HC_Meadow_results_metastats)

#Heat_results <- merge(top.table_H_intersect,FC.selecDE, H_results_metastats, by.x = "Gene", all.x = TRUE)
HeatMeadow_results <- merge(top.table_HC_Meadow,FC.selecDE, all.x = TRUE)
HeatMeadow_results2 <- merge(HeatMeadow_results, HC_Meadow_results_metastats, all.x = TRUE)
head(HeatMeadow_results2)

write.table(HeatMeadow_results2, file = "OutputFiles/Meta_Heat-Control_Meadow.txt", row.names = F, sep = "\t", quote = F)

Make summary tables

Heat vs Control in Meadow (slow-living) ecotype

## Make a table of results
fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)]
invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)]
indstudy_de <- list(rownames(keepDE)[which(keepDE[,"DE.2008Dataset"]==1)],rownames(keepDE)[which(keepDE[,"DE.2012Dataset"]==1)])
IDD.IRR(fishcomb_de,indstudy_de)

    DE    IDD   Loss    IDR    IRR 
362.00 109.00   0.00  30.11   0.00 

IDD.IRR(invnorm_de, indstudy_de)

    DE    IDD   Loss    IDR    IRR 
340.00 105.00  18.00  30.88   7.11 

Make Upset Plot, Treatment in Slow-living

# Heat Treatment 2008
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_HC_Meadow08_top <- top.table_HC_Med08[which(top.table_HC_Med08$adj.P.Val<=0.05), ]
dim(UP_HC_Meadow08_top)

[1] 7 7

  # converting column of "Gene" in dataframe column into vector by passing as index
HC_Meadow_HS2008 = UP_HC_Meadow08_top[['Gene']]
# Heat Treatment 2012
  #Subset the top.tables to only contain the rows where "adj.P.Val <=0.05
UP_HC_Meadow12_top <- top.table_HC_Med12[which(top.table_HC_Med12$adj.P.Val<=0.05), ]
dim(UP_HC_Meadow12_top)

[1] 397   7

  #converting column of "Gene" in dataframe column into vector by passing as index
HC_Meadow_HS2012 = UP_HC_Meadow12_top[['Gene']]
# Heat MetaAnalysis
## Keep the genes from the meta-analysis that have invernorm adjusted p-value <= 0.05 and the log fold change is going in the same direction in both analyses
UP_MetaA <- HeatMeadow_results2[which(HeatMeadow_results2$DE.invnorm_adj_P<=0.05), ]
UP_MetaB <- UP_MetaA[which(abs(UP_MetaA$signFC)==1),]
dim(UP_MetaB)

[1] 340  23

  #converting column of "Gene" in dataframe column into vector by passing as index
HC_Meadow_Meta = UP_MetaB[['Gene']]

listHC_Meadow <- list(HS2008 = HC_Meadow_HS2008, HS2012 = HC_Meadow_HS2012, MetaAnalysis = HC_Meadow_Meta)

# Make upset put and save as a .pdf file
pdf("OutputGraphs/Upset_Heat-Control_Meadow.pdf")
  #make plot
upset(fromList(listHC_Meadow), mainbar.y.label = "Number of Genes in Intersection", sets.x.label = "Differentially expressed genes (adjusted p-value=0.05)",order.by = "freq")
dev.off()

null device 
          1 

Merge top tables results, Categories, GSEA calculations

MetaAnalysis Results files: Heat_results2 Ecotype_results2 Interaction_results2 HeatLake_results2 HeatMeadow_results2 EcotypeControl_results2 EcotypeHeat_results2

#Libraries needed for this section
library(plyr)
library(data.table)

Pull in Categories from Sequence Capure

categories <- as.data.frame(read.csv("Input/Information/SeqCapGene_Categories_NewManuscript.csv"))
head(categories)

dim(categories)

[1] 396   5

Make log counts per million to eventually merge incase need it for downstream enrichment analysis

logcpm2012 <- cpm(x2_2012, log=TRUE)
logcpm2012 <- as.data.frame(logcpm2012)
logcpm2012$Gene<-rownames(logcpm2012)

Heat Results

Calculate GSEA Rank for HS0012 dataset

top.table_Heat12_SignRank <-  within(top.table_Heat12, rank <- sign(logFC) * -log10(P.Value))
dim(top.table_Heat12_SignRank)

[1] 14431     8

Merge All results: HS2008, HS2012 with Rank, MetaAnalysis

dim(GSEA_Rank_Heat_HS2012)

[1] 10602     2

Ecotype Results

Calculate GSEA Rank for HS0012 dataset

top.table_Ecotype12_SignRank <-  within(top.table_Eco12, rank <- sign(logFC) * -log10(P.Value))
dim(top.table_Ecotype12_SignRank)

[1] 14431     8

Merge All results: HS2008, HS2012 with Rank, MetaAnalysis

dim(GSEA_Rank_Ecotype_HS2012)

[1] 10602     2

Interaction Results

Calculate GSEA Rank for HS0012 dataset

top.table_I_2012_SignRank <-  within(top.table_I_2012, rank <- sign(logFC) * -log10(P.Value))
dim(top.table_I_2012_SignRank)

[1] 14431     8

Merge All results: HS2008, HS2012 with Rank, MetaAnalysis

GSEA_Rank_Interaction_HS2012 <- grepl.sub(data = GSEA_Rank_Interaction_HS2012, pattern = "LOC1165*", Var = "Gene", keep.found = FALSE)
dim(GSEA_Rank_Interaction_HS2012)

[1] 10602     2

Heat-Control_Lake Results

Calculate GSEA Rank for HS0012 dataset

top.table_HC_Lake12_SignRank <-  within(top.table_HC_Lake12, rank <- sign(logFC) * -log10(P.Value))
dim(top.table_HC_Lake12_SignRank)

[1] 14431     8

Merge All results: HS2008, HS2012 with Rank, MetaAnalysis

dim(GSEA_Rank_HeatControl_Lake_HS2012)

[1] 10602     2

Heat-Control_Meadow Results

Calculate GSEA Rank for HS0012 dataset

top.table_HC_Med12_SignRank <-  within(top.table_HC_Med12, rank <- sign(logFC) * -log10(P.Value))
dim(top.table_HC_Med12_SignRank)

[1] 14431     8

Merge All results: HS2008, HS2012 with Rank, MetaAnalysis

# Write the file as a tab delimited file with .rnk for import into GSEA
write.table(as.table(GSEA_Rank_HeatControl_Meadow_HS2012), file="OutputFiles/GSEA_Rank_HeatControl_Meadow_HS2012.rnk", sep="\t", quote = FALSE) 

Error in as.table.default(GSEA_Rank_HeatControl_Meadow_HS2012) : 
  cannot coerce to a table

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